prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: Who was Ralph Rokebye's brother?

Read the following text and answer briefly.

Context 1: Rokebye, Ralph of Yorks, arm  Gloucester Hall, matric  9 Nov , 1582, aged 15; student of Lincoln's Inn 1585  See Foster's Inns of Court Register 
Rokebye, Ralph of Herts (? Yorks), gent  Broadgates Hall, matric  entry 28 Feb , 1589-90, aged 14  See pedigree in Foster's Yorkshire Collection 
Rokeby, William (brother of Sir Richard, treasurer of Ireland, son of John, of Thundercliffe Grange, Yorks), fellow of King's Hall, Cambridge, D Can L ; rector of Sandal 1487, and of Halifax, Yorks, 1502, rector of Fakenham, Norfolk, 1496, chancellor of Ireland 1498, and 1515, bishop of Meath, and privy councillor 1507, archbishop of Dublin 1512, archdeacon of Surrey 1520, until his death 29 Nov , 1521  See Ath  ii  717; Cotton, i  25; & Lansdowne MS  979, ff  4, 6 
Rolfe, Augustine (Rolfus) M A  from Queen's Coll , Cambridge, 1595; incorporated 10 July, 1599 
Rolf, Richard B A  from Emanuel Coll , Cambridge, 1584-5 (incorporated 11 July, 1585); M A  1588  See Foster's Graduati Cantab 
Rolfe, William cler  fil  New Coll , matric  10 March, 1656-7, B A  1660, fellow, M A  14 Jan , 1663-4; rector of Brampton 1668, and of Stoke Bruern, Northants, 1676, until his death, buried (at Stoke Bruern) 6 Sept , 1693  See Baker's Northants, i  86 
Rolfe, William s  William, of Stoke-Bruern, Northants, cler  Brasenose Coll  7 July, 1688, aged 16; student of Inner Temple 1692, buried in the Temple church 1 March, 1692-3  See Foster's Inns of Court Reg 
Rolle, Denis youngest son John, of Steventon, Devon, equitis  Exeter Coll , matric  15 Feb , 1666-7, aged 17; brother of John same date 
Rolle, Denis s  D , of Heanton, Devon, arm  Exeter Coll  matric  24 Oct , 1687, aged 17; B A  1691, M A  1694 (as Denys), rector of Merton, Devon, 1696  See Samuel 1687, & Foster's Index Ecclesiasticus 
Rolle, (Sir) Henry of Devon, arm  fil  Broadgates Hall, matric  14 June, 1594, aged 18; student of Middle Temple 1597 (as son and heir of Henry, of Steventon, Devon, esq ), knighted 23 July, 1603, died in 1617  See Foster's Inns of Court Reg 
Rolle, Henry of Devon, arm  Exeter Coll , matric  20 March, 1606-7, aged 17; bar -at-law, Inner Temple, 1618, bencher 1633 (2s  Robert, of Heanton, Devon), M P  Callington 1621-2, 1624-5, Truro 1625-1626, 1628-9, serjeant-at-law 1640, recorder of Dorchester 1636, a judge of king's bench 1645, chief justice of upper bench 1648-55, died 30 July, 1656, buried in Shapwick church, Somerset; brother of Samuel 1605  See Ath  iii  416; & Foster's Judges and Barristers 
Rolle, Henry s  Alex , of Tavistock, Devon, gent  Christ Church, matric  23 March, 1696-7, aged 17 
Rolle, John of Devon, arm  Exeter Coll , matric  30 May, 1589, aged 15; B A  8 Feb , 1592-3, M A  25 May, 1596 
Rolle, John 1s  John, of Steventon, Devon, equitis  Exeter Coll , matric  15 Feb , 1666-7, aged 18; of Bicton, Devon; died in his father's lifetime, buried at Bicton 22 April, 1689; brother of Denis 1667, and father of Denis 1698 
Rolle, Richard s  Richard, of Cookeburye, Devon, gent  New Inn Hall, matric  26 Sept , 1634, aged 18; B A  from Jesus Coll , Cambridge, 1638, incorporated from Gloucester Hall 17 Dec , 1639, M A  2 July, 1642, rector of Sheviocke, Cornwall, 1656; father of the next-named  See Foster's Index Eccl 
Rolle, Richard s  R , of Sheviock, Cornwall, cler  St  Alban Hall, matric  3 July, 1674, aged 17; B A  1678 
Rolle, Robert (Rooles or Roales) fellow New Coll  1551-60 from Mark Lane, city of London, B A  26 June, 1555, M A  26 July, 1560, B D  22 Jan , 1572-3, D D  June, 1585, a teacher in Westminster school; perhaps canon of Combe (4) in Wells, 1574, and rector of Stoke Climsland, Devon, 1574  See O H S  i  345; & Foster's Index Eccl 
Rolle, Samuel s  Denis, of Great Torrington, Devon, arm  Exeter Coll , matric  16 July, 1687, aged 18, B A  1691; bar -at-law Middle Temple 1697; M P  Barnstaple 1705, died 1747; see Denis 1687  See Foster's Judges and Barristers 
Rolle, William B C L  14 July, 1528; perhaps vicar of Yarncombe, Devon, 1536  See Foster's Index Ecclesiasticus 
Rolles, Gabriel (Rooles) B A  from St  John's Coll , Cambridge, 1610-11, M A  1614; incorporated 13 July, 1619, rector of East Locking, Berks, 1620, as Rolle  See Foster's Graduati Cantab 
Rolles, Richard gent  Jesus Coll , matric  1 March, 1632-3, B A  next day, M A  15 Oct , 1635; perhaps created B D  20 Dec  1642, "ex regis gratia," rector of Wavendon, Bucks, and of Witham, Essex, 1646, by the Westminster assembly  See Add  MS  15,670, p  70 
Rolles, William s  Richard, of Lewknor, Oxon, gent  St  John's Coll , matrie  12 March, 1637-8, aged 17, B A  9 Nov , 1641, M A  6 July, 1644; B D  from Jesus Coll  12 Sept , 1661, rector of Wheatfield, Oxon, 1660, and of Chalfont St  Giles, Bucks, 1662  See Foster's Index Eccl 
Rolles, William created M A  from Exeter Coll  14 April, 1648 
Rolleston, Simon created M A  31 Aug , 1636 
Rolleston, Thomas of Devon, gent  Wadham Coll , matric  12 May, 1620, aged 16 
Rollinson, Francis 1584  See Rallinson 
Rollinson, William s  "Jose," of London, gent  St  John's Coll , matric  7 March, 1694-5, aged 15; perhaps brother of John Rawlinson, of New Coll  1692  See page 1236 
Rolt, Edward youngest son of Tho , of London, equitis  Merton Coll , matric  7 Nov , 1701, aged 15; of Sacomb, Herts, and Chippenham, Wilts, student of Lincoln's Inn, 1702, M P  St  Mawes 1713, Grantham 1715-22, Chippenham 1722; died 22 Dec , 1722; his father knighted 1 Oct , 1682, and died 9 Sept , 1710  See Foster's Parliamentary Dictionary 
Rolte, George s  Thomas, of St  Margarets par  Darenth, Kent, pleb  St  Alban Hall, matric  17 June, 1631, aged 18; B A  20 June, 1631, M A  29 April, 1634, incorporated at Cambridge 1639 
Romane, Edmund pleb  Balliol Coll , matric  20 Feb , 1627-8, aged 18; B A  next day, M A  3 June, 1630 
Romaine, Matthew pleb  Balliol Coll , matric  10 June, 1630, B A  same day, M A  14 May, 1633, vicar of Stoke Gaylard, Dorset, 1639; father of the next  See Foster's Index Eccl 
Romayne, Thomas s  Matth , of Stoke Gaylard, Dorset, minister  Wadham Coll , matric  17 July, 1669, aged 17; B A  from Hart Hall 1673, "the intruded" rector of Stoke Gaylard 1675  See Foster's Index Eccl 
Romayne, William (Ronayne) gent  Trinity Coll , matric  31 July, 1671, aged 16 
Rome, Harcourt s  William, of London, p p  Brasenose Coll , matric  13 Dec , 1672, aged 17 
Rome, William s  G  (? "Gul "), of Northampton (city), pleb  Brasenose Coll , matric  11 Dec , 1684, aged 16 
Romney, Joseph B A  from Emanuel Coll , Cambridge, 1610-11, M A  1614; incorporated 8 July, 1614, student of Inner Temple 1610, as of London, gent  See Foster's Inns of Court Reg 
Rone, John s  Randolph, of Hanmer, Flints, pleb  Brasenose Coll , matric  10 Oct , 1634, aged 18; D D  Trinity Coll , Dublin, 25 Jan , 1666 (as Roane), vicar of Hanmer, Flints, 1644, ejected same year, dean of Clogher 1667, bishop of Killaloe 1675, until his death 5 Sept , 1692  See Cotton's Fasti Ecc  Hib  i  467 
Rone, William of New Coll  1661  See Roane 
Roode, Edward (or Rode) B A  21 July, 1522, M A  26 Nov , 1534; perhaps canon of Southwell 1561-73 
Roode, Edward cler  fil  Merton Coll , matric  22 Nov , 1650; Eton postmaster 1649, fellow 1651, B A  2 March, 1651-2, M A  14 Dec , 1655; incorporated at Cambridge 1657, and LL D  1671; vicar of Gamlingay, co  Cambridge, rector of one moiety 1661, and of the other 1677; died at Cambridge 1689  See Burrows, 525; & O H S  iv  292 
Roode, Onesiphorus s  Edward, of Thame, Oxon, sacerd  New Inn Hall, matric  27 Oct , 1637, aged 16, B A  1 July, 1641; incorporated at Cambridge 1645; chaplain to the house of lords after the expulsion of the bishops; minister of New chapel, Tuttle-Fields, Westminster, 1648, until ejected in 1660  See Calamy, i  195 
Rood, Richard M A  from Pembroke Coll  5 Dec , 1634 
Rooke, John s  Tho , of Broadwell, co  Gloucester, pleb  Pembroke Coll , matric  1 March, 1683-4, aged 17; brother of Thomas 1693 
Rooke, John s  Tho , of Whitchurch, Wilts, gent  Balliol Coll , matric  14 Jan , 1713-14, aged 17 
Rooke, Nicholas s  Arthur, of Totnes, Devon, gent  Exeter Coll , matric  10 March, 1670-1, aged 16; B A  1674, M A  1677, rector of Dartington, Devon, 1679  See Foster's Index Eccl 
Rooke, Robert "ser " Oriel Coll , matric  1 April, 1656, B A  1659 
Rooke, Robert s  R , p p  St  Alban Hall, matric  30 March, 1677, aged 17 
Rooke, Thomas pleb  Christ Church, matric  3 May, 1659 
Rooke, William (Roock) of Dorset, pleb  Brasenose Coll , matric  entry under date 20 March, 1578-9, aged 19; B A  from St  Alban Hall 30 Jan , 1582-3, M A  9 May, 1586 
Rooke, William of Dorset, gent  New Coll , matric  12 July, 1605, aged 18; B A  21 Feb , 1608-9, chaplain, M A  16 Dec , 1611, rector of North Cheriton, Somerset, 1618  See Foster's Index Eccl 
Rooke, William s  J , of Workington, Cumberland, p p  Queen's Coll , matric  22 Oct , 1669, aged 17; B A  1674, M A  1677, B D  1690, vicar of Plumstead, Kent, 1691, and rector of Hadley, Hants, 1695  See Foster's Index Eccl 
Rookes, Christopher (Rokys or Rokkis) B A  8 July, 1522, M A  1 July, 1527, B D  supd  Oct , 1540; principal of Magdalen Hall 1529-32, vicar of Stanstead Abbots, Herts, 1534  See Foster's Index Eccl 
Rookes, Jonas B A  from Magdalen Hall 24 April, 1599, M A  11 Feb , 1601-2 (2s  William, of Roydes Hall); vicar of Penistone, Yorks, 1619, see Foster's Index Eccl ; styled fellow and bursar of University Coll  in Foster's Yorkshire Collection, possibly brother of the next-named 
Rookes, Robert of Yorks, pleb  Magdalen Hall, matric  14 May, 1602, aged 19; possibly brother of the last-named 
Ro(o)kes, William demy Magdalen Coll  1544, B A  supd  1551, fellow 1552-71, M A  27 April, 1556, B Med  supd  24 April, 1561  See Bloxam, iv  99 
Rookes, William s  William, of Rhodes Hall, Yorks, gent  University Coll , matric  30 June, 1665, aged 16; died at Oxford in 1667 
Roope, Ambrose s  A , of Dartmouth Parva, Devon, arm  Exeter Coll , matric  15 March, 1671-2, aged 16 
Roope, George s  Ant , of Bradford, Wilts, gent  Hart Hall, matric  10 Oct , 1702, aged 15 
Roope, John s  Nicholas, of Dartmouth, Devon, gent  Exeter Coll , matric  17 Nov , 1637, aged 15; student of Lincoln's Inn 1638  See Foster's Inns of Court Reg 
Roope, Nicholas of Devon, gent  Broadgates Hall, matric  6 Feb , 1606-7, aged 18; B A  6 Nov , 1610; probably father of the last-named 
Rooper, Thomas s  T , of London, gent  Trinity Coll , matric  9 July, 1699, aged 16; B A  1703, M A  19 Feb , 1705-6, as Roper 
Rooper, William of St  Alban Hall 1667  See Roper 
Roos, Brian D Can L  or doctor of decrees of the university of Valentia; incorporated 3 Feb , 1510-11; died 1529, buried in the church of Chelray  See Fasti, i  31 
Root, Isaac pleb  St  John's Coll , matric  2 July, 1658, admitted to Merchant Taylors' school 1649 (only son of Isaac, merchant taylor); born in Trinity parish 20 Aug , 1641  See Robinson, i  193 
Roots, Richard s  Tho , of Tunbridge, Kent, gent  St  John's Coll , matric  26 Dec , 1689, aged 15; demy Magdalen Coll  1690-1702, B A  1693, M A  1696, rector of Chilmarck, Wilts, 1702-27, canon of Sarum 1722, rector and vicar of Bishopstone, Wilts, 1728; brother of William 1699  See Rawl  iii  447, and xix  90; Bloxam, vi  111; & Foster's Index Eccl 
Roots, Thomas of Sussex, pleb  Magdalen Hall, matric  entry 17 Nov , 1581, aged 13; B A  supd  1 July, 1584, bar -at-law, Lincoln's Inn, 1594  See Foster's Judges and Barristers 
Rootes, Thomas s  William, of Tunbridge, Kent, pleb  St  John's Coll , matric  31 Jan , 1628-9, aged 23; B A  12 Feb , 1628-9, vicar of Long Stanton All Saints, co  Cambridge, 1630  See Add  MSS  15,669-70; & Foster's Index Eccl 
Rootes, Thomas pleb  St  John's Coll , matric  2 July, 1658; B A  1661, M A  1666; possibly father of Richard 1689, and William 1699 
Roots, William s  Tho , of Tunbridge, Kent, gent  Christ Church, matric  16 March, 1698-9, aged 18; B A  1704; clerk Magdalen Coll  1705-11, M A  1707, rector of Little Berkhampstead, Herts, 1714; brother of Richard 1689  See Bloxam, ii  85; & Foster's Index Eccl 
Roper, Francis s  Robert, of Trimdon, co  Durham, gent  Corpus Christi Coll , matric  16 Dec , 1661, aged 18; probably identical with Francis, son of Robert, of Kelloe, co  Durham, farmer, was admitted sizar of St  John's Coll , Cambridge, 21 Sept , 1658, aged 16; fellow, B A  1662-3, M A  1666, B D  1673, vicar of Waterbeach, co  Cambridge, 1678, canon of Ely 1686-90, rector of Northwold, Norfolk, 1687, died 13 April, 1719  See Mayor, 138; Surtees' Durham, i  107; & Foster's Index Eccl 
Roper, John (or Rooper) demy Magdalen Coll , from Berks, M A  fellow, 1483, D D  disp  27 June, 1506, (first) Margaret professor of divinity, 1500, vice-chancellor of the university 1505, and 1511, principal of Salesurry and George Hall, rector of Witney, Oxon, 1493, vicar of St  Mary's church, Oxford, canon of Cardinal Coll  1532; died May, 1534  See Ath  i  76; & Landsowne MS  979, f  118 
Roper, John B A  disp  4 July, 1512 
Roper, Thomas of Trinity Coll  1699  See Rooper 
Roper, Philip of Kent, arm  Gloucester Hall, matric  7 Sept , 1588, aged 15 (subscribes Rooper) 
Roper, William (subscribes Rooper) of co  Hereford, militis fil  St  Alban Hall, matric  entry dated 5 June, 1607, aged 13; probably of Malmains, Kent, 2nd son of Sir Christopher Roper, afterwards 2nd baron Teynham  See Foster's Peerage 
Roscarrock, Henry of Cornwall, arm  Hart Hall, matric  entry under date 17 Dec , 1576, aged 21; probably son of Thomas, of Roscarrock, and brother of the next, and of Richard 1581 
Roscarrock, John B A  11 Feb , 1576-7; perhaps from Exeter Coll  (and 1s  Thomas, of Roscarrock, Cornwall); died 24 Nov , 1608; brother of Henry and Richard  See O H S  xii  65 
Roscarrock, Nicolas (Roiscariot) B A  supd  3 May, 1568, student Inner Temple 1571, as of Roscarrock, Cornwall  See Foster's Inns of Court Reg 
Roscarrock, Richard of Cornwall, arm  Broadgates Hall, matric  entry under date circa 1581, aged 19; student of Middle Temple 1583 (as 3s  Thomas, of Roscarrock, Cornwall, esq ), brother of Henry and John  See Foster's Inns of Court Reg 
Rosdell, Christopher of Yorks, pleb  St  Edmund Hall, matric  entry under date 22 Dec , 1576, aged 22, B A  4 July, 1576; rector of St  Bennet Sherehog, London, 1579, and vicar of Somerton, Somerset, 1582  See Foster's Index Eccl 
Rose, Christopher s  John, of Marlow, Bucks, gent  Christ Church, matric  13 Feb , 1622-3, aged 21, B A  same day; rector of Hutton, Essex, 1642  See Foster's Index Ecclesiasticus 
Rose, Christopher s  Giles, of Lynn Regis, Norfolk, gent  Lincoln Coll , matric  8 July, 1670, aged 15; student of Gray's Inn, 1673  See Foster's Gray's Inn Register 
Rose, Gilbert Augustinian Canon, B D  supd  22 May, 1512, and supd  12 Dec , 1519, for incorporation as D D 
Rose, Henry "ser " Lincoln Coll , matric  22 July, 1658, B A  16 Jan , 1660-1, fellow 1662 from Pirton, Oxon, M A  1663 (incorporated at Cambridge 1688), B D  1672; minister of All Saints, Oxford, but running much into debt, and marrying beneath himself, left his fellowship and church about 1674, retired to London, and at length to Ireland  See Ath  iv  561 
Rose, Hugh s  "Dav  Ni " (Nigg 4to ), of Ross, Scotland, p p  (subs  pleb )  Balliol Coll , matric  3 April, 1707, aged 20; B A  1709 
Rose, John B A  8 June, 1519, fellow Merton Coll  1523, M A  31 March, 1525; one of these names vicar of Shoreham, Kent, 1536  See Foster's Index Ecclesiasticus 
Rose, John of co  Leicester, pleb  Merton Coll , matric  24 Nov , 1581, aged 21 
Rose, John s  Jeremy, of Swell, co  Gloucester, pleb  Corpus Christi Coll , matric  12 Dec , 1623, aged 15; B A  4 July, 1626 
Rose, John s  Rich , of Halberton, Devon, gent  Exeter Coll , matric  14 May, 1688, aged 17 
Rose, John s  J , of West Derby, co  Lancaster, pleb  University Coll , matric  7 March, 1712-13, aged 18, B A  1716; rector of Bilborough, Notts, 1722  See Foster's Index Eccl 
Rose, Jonathan s  Th , of Mickleton, co  Gloucester, pleb  St  Alban Hall, matric  16 May, 1677, aged 18; B A  9 Feb , 1680-1 
Rose, Joseph s  Thomas, of Sturminster Newton, Dorset, pleb  Oriel Coll , matric  12 Dec , 1623, aged 19 
Rose, Richard B A  from Exeter Coll  14 June, 1621; perhaps student of Middle Temple 1622 (as son and heir of John, of Lyme, Dorset, gent ), and M P  Lyme Regis April-May, 1640, 1640 (l p ), till his death after 1648  See Foster's Inns of Court Reg  & Foster's Parliamentary Dictionary 
Rose, Richard arm  Exeter Coll , matric  29 March, 1656; student of Lincoln's Inn 1659, as 4s  Richard, of Wootton Fitzwarren, Dorset, esq  See Foster's Inns of Court Reg 
Rose, Richard s  Richard, of Monks Kirby, co  Warwick, pleb  Magdalen Coll , matric  3 May, 1672, aged 16 (as Rosse); chorister 1670-6  See Bloxam, i  95 
Rose, Richard s  R(ichard), of Wyng, Bucks, gent  Trinity Coll , matric  7 May, 1680, aged 16; bar -at-law, Inner Temple, 1699  See Foster's Judges and Barristers 
Rose, Stephen of co  Gloucester, pleb  Corpus Christi Coll , matric  21 Jan , 1619-20, aged 16; B A  13 Nov , 1621, M A  2 July, 1625, vicar of Aldermaston 1627, and rector of Barkham 1633, and of Arborfield, Berks, 1640, and perhaps of Hartley Mawditt, Hants, 1652  See Foster's Index Ecclesiasticus 
Rose, Stephen "ser " Lincoln Coll , matric  19 Nov , 1650 
Rose, Stephen "servi  fil " Magdalen Coll , matric  19 Nov , 1650 (subscribes "serv ") 
Rose, Stephen "ser " Magdalen Coll , subscribed 23 Nov , 1655; B A  from Wadham Coll  1659, vicar of Cold Overton, co  Leicester, 1662-3, and rector of Woolhampton, Berks, 1667-95, father of Temple  See Foster's Index Eccl 
Rose, Temple s  Step , of Woolhampton, Berks, cler  Trinity Coll , matric  29 March, 1693, aged 17, B A  1696 
Rose, Thomas Minorite, B D  22 June, 1509 
Rose, Thomas of Herts, pleb  Magdalen Hall, matric  10 Oct , 1589, aged 15 
Rose, Thomas s  Seth, of Telscombe, Sussex, sacerd  Oriel Coll , matric  5 June, 1640, aged 18; his father rector of Telscombe 1604, etc  See Foster's Index Eccl 
Rose, Thomas s  Edw , 
Context 2: \section{Introduction and main results}


In this note we are interested in the existence versus non-existence of stable sub- and super-solutions of equations of the form
\begin{equation} \label{eq1}
-div( \omega_1(x) \nabla u ) = \omega_2(x) f(u) \qquad \mbox{in $ {\mathbb{R}}^N$,}
\end{equation} where $f(u)$ is one of the following non-linearities: $e^u$,  $ u^p$ where $ p>1$ and $ -u^{-p}$ where $ p>0$   We assume that $ \omega_1(x)$ and $ \omega_2(x)$, which we call \emph{weights},  are smooth positive functions (we allow $ \omega_2$ to be zero at say a point) and which satisfy various growth conditions at $ \infty$     Recall that we say that a solution $ u $ of $ -\Delta u = f(u)$ in $ {\mathbb{R}}^N$ is stable provided
\[ \int f'(u) \psi^2 \le \int | \nabla \psi|^2, \qquad \forall \psi \in C_c^2,\] where $ C_c^2$ is the set of $ C^2$ functions defined on $ {\mathbb{R}}^N$ with compact support   Note that the stability of $u$ is just saying that the second variation at $u$ of the energy associated with the equation is non-negative      In our setting this becomes:  We say a $C^2$ sub/super-solution $u$ of (\ref{eq1}) is \emph{stable} provided
\begin{equation} \label{stable}
\int \omega_2 f'(u) \psi^2 \le \int \omega_1 | \nabla \psi|^2 \qquad \forall \psi \in C_c^2 
\end{equation}
One should note that (\ref{eq1}) can be re-written as
\begin{equation*}
- \Delta u  + \nabla \gamma(x) \cdot \nabla u ={ \omega_2}/{\omega_1}\ f(u) \qquad \text{ in $ \mathbb{R}^N$},
\end{equation*}
where
$\gamma = - \log( \omega_1)$ and on occasion we shall take this point of view 


\begin{remark} \label{triv} Note that  if $ \omega_1$ has enough integrability then it is immediate that if $u$ is a stable solution  of (\ref{eq1}) we have $ \int \omega_2 f'(u) =0 $  (provided $f$ is increasing)    To see this let $ 0 \le \psi \le 1$ be supported in a ball of radius $2R$ centered at the origin ($B_{2R}$) with $ \psi =1$ on $ B_R$ and such that $  | \nabla \psi | \le \frac{C}{R}$ where $ C>0$ is independent of $ R$    Putting this $ \psi$ into $ (\ref{stable})$ one obtains
\[ \int_{B_R} \omega_2 f'(u) \le \frac{C}{R^2} \int_{R < |x| <2R} \omega_1,\] and so if the right hand side goes to zero as $ R \rightarrow \infty$ we have the desired result 

\end{remark}





The existence versus non-existence of stable solutions of  $ -\Delta u =  f(u)$ in $ {\mathbb{R}}^N$ or $ -\Delta u = g(x) f(u)$ in $ {\mathbb{R}}^N$ is now quite well understood, see  \cite{dancer1, farina1, egg, zz, f2, f3, wei, ces, e1, e2}   We remark that some of these results are examining the case where $ \Delta $ is replaced with $ \Delta_p$ (the $p$-Laplacian) and also in many cases the authors are interested in finite Morse index solutions or solutions which are stable outside a compact set 
  Much of the interest in these Liouville type theorems  stems from the fact that the non-existence of a stable solution is related to the existence of a priori estimates for stable solutions of a related equation on a bounded domain 




       In \cite{Ni}  equations similar to  $ -\Delta u = |x|^\alpha u^p$  where examined on the unit ball in $ {\mathbb{R}}^N$ with zero Dirichlet boundary conditions   There it was shown that for $ \alpha >0$ that  one can obtain positive solutions for $ p $ supercritical with respect to Sobolev embedding and so one can view that the term $ |x|^\alpha$ is restoring some compactness       A similar feature happens for equations of the form
\[ -\Delta u = |x|^\alpha f(u) \qquad \mbox{in $ {\mathbb{R}}^N$};\]    the value of $ \alpha$ can vastly alter the existence versus non-existence of a stable solution,  see \cite{e1, ces, e2, zz, egg} 

We now come to our main results and for this we need to define a few quantities:

\begin{eqnarray*}
I_G&:=& R^{-4t-2} \int_{ R < |x|<2R} \frac{ \omega_1^{2t+1}}{\omega_2^{2t}}dx , \\
 J_G&:=& R^{-2t-1} \int_{R < |x| <2R} \frac{| \nabla \omega_1|^{2t+1} }{\omega_2^{2t}} dx  ,\\I_L&:=& R^\frac{-2(2t+p-1)}{p-1}  \int_{R<|x|<2R }{ \left(   \frac{w_1^{p+2t-1}}{w_2^{2t}}    \right)^{\frac{1}{p-1} }  }  dx,\\ J_L&:= &R^{-\frac{p+2t-1}{p-1} }  \int_{R<|x|<2R }{ \left(   \frac{|\nabla w_1|^{p+2t-1}}{w_2^{2t}}     \right)^{\frac{1}{p-1} }  }  dx,\\
I_M  &:=&   R^{-2\frac{p+2t+1}{p+1} }  \int_{R<|x|<2R }{ \left(   \frac{w_1^{p+2t+1}}{w_2^{2t}}    \right)^{\frac{1}{p+1} }  } \ dx, \\
J_M  &:= &  R^{-\frac{p+2t+1}{p+1} }  \int_{R<|x|<2R }{ \left(   \frac{|\nabla w_1|^{p+2t+1}}{w_2^{2t}}     \right)^{\frac{1}{p+1} }  }  dx 
\end{eqnarray*}


The three equations we examine are
\[ -div( \omega_1 \nabla u ) = \omega_2 e^u \qquad \mbox{ in $ {\mathbb{R}}^N$ } \quad  (G), \]
\[  -div( \omega_1 \nabla u ) = \omega_2 u^p \qquad \mbox{ in $ {\mathbb{R}}^N$ } \quad  (L), \]
\[ -div( \omega_1 \nabla u ) = - \omega_2 u^{-p} \qquad \mbox{ in $ {\mathbb{R}}^N$ } \quad  (M),\]  and where we restrict $(L)$ to the case $ p>1$ and $(M)$ to $ p>0$     By solution we always mean a $C^2$ solution    We now come to our main results in terms of abstract $ \omega_1 $ and $ \omega_2$     We remark that our approach to non-existence of stable solutions is the approach due to Farina, see \cite{f2,f3,farina1} 

\begin{thm} \label{main_non_exist} \begin{enumerate}


\item   There is no  stable sub-solution of $(G)$ if $ I_G, J_G \rightarrow 0$ as $ R \rightarrow \infty$   for some   $0<t<2$ 
 \item    There is no positive   stable sub-solution (super-solution) of $(L)$  if  $ I_L,J_L \rightarrow 0$ as $ R \rightarrow \infty$   for some   $p- \sqrt{p(p-1)}   < t<p+\sqrt{p(p-1)} $  ($0<t<\frac{1}{2}$) 
\item   There is no positive   stable super-solution of (M) if  $ I_M,J_M \rightarrow 0$ as $ R \rightarrow \infty$  for some $0<t<p+\sqrt{p(p+1)}$ 

\end{enumerate}

\end{thm}     If we assume that $ \omega_1$ has some monotonicity we can do  better    We will assume that the monotonicity conditions is satisfied for big $x$  but really all ones needs is for it to be satisfied on a suitable sequence of annuli 

\begin{thm} \label{mono}  \begin{enumerate} \item There is no  stable sub-solution of $(G)$ with $  \nabla \omega_1(x) \cdot  x \le 0$ for big $x$ if  $ I_G \rightarrow 0$ as $ R \rightarrow \infty$   for some   $0<t<2$ 

\item There is no positive  stable sub-solution of  $(L)$ provided  $  I_L \rightarrow 0$ as $ R \rightarrow \infty$ for either:
\begin{itemize}
\item  some $  1 \le t < p + \sqrt{p(p-1)}$ and $  \nabla \omega_1(x) \cdot x \le 0$ for big $x$,  or  \\

\item some $ p - \sqrt{p(p-1)} < t \le 1$ and $ \nabla \omega_1(x) \cdot x \ge 0$ for big $ x$ 
\end{itemize}
 There is no positive super-solution of $(L)$ provided $ I_L \rightarrow 0$ as $ R \rightarrow \infty$ for some $ 0 < t < \frac{1}{2}$ and $ \nabla \omega_1(x) \cdot x \le 0$ for big $x$ 



\item  There is no positive stable super-solution of $(M)$ provided  $ I_M \rightarrow 0$ as $ R \rightarrow \infty$  for some  $0<t<p+\sqrt{p(p+1)}$ 

\end{enumerate}

\end{thm}


\begin{cor}  \label{thing}  Suppose $ \omega_1 \le C  \omega_2$  for big $ x$, $ \omega_2 \in L^\infty$,  $ \nabla \omega_1(x) \cdot  x \le 0$ for big $ x$ 
\begin{enumerate} \item There is no stable sub-solution of $(G)$ if $ N \le 9$ 

\item  There is no positive stable sub-solution of $(L)$ if  $$N<2+\frac{4}{p-1} \left( p+\sqrt{p(p-1)}  \right) $$

\item  There is no positive stable super-solution of $(M)$ if $$N<2+\frac{4}{p+1} \left( p+\sqrt{p(p+1)}  \right) $$

\end{enumerate}

\end{cor}


If one takes $ \omega_1=\omega_2=1$ in the above corollary, the results obtained for $(G)$ and  $(L)$,  and for some values of $p$ in $(M)$, are optimal, see \cite{f2,f3,zz} 





We now drop all monotonicity conditions on $ \omega_1$ 

\begin{cor} \label{po} Suppose  $ \omega_1 \le C \omega_2$ for big $x$, $ \omega_2 \in L^\infty$, $ | \nabla \omega_1| \le C \omega_2$ for big $x$ 
\begin{enumerate} \item  There is no stable sub-solution of $(G)$ if $ N \le 4$ 

\item  There is no positive stable sub-solution of $(L)$ if $$N<1+\frac{2}{p-1} \left( p+\sqrt{p(p-1)}  \right) $$

\item There is no positive super-solution of $(M)$ if $$N<1+\frac{2}{p+1} \left( p+\sqrt{p(p+1)}  \right) $$

\end{enumerate}

\end{cor}

Some of the conditions on $ \omega_i$ in Corollary \ref{po} seem somewhat artificial   If we shift over to the advection equation (and we take $ \omega_1=\omega_2$  for simplicity)
\[ -\Delta u + \nabla \gamma \cdot \nabla u = f(u), \] the conditions on $ \gamma$ become: $ \gamma$ is bounded from below and has a bounded gradient 



In what follows we examine the case where $ \omega_1(x) = (|x|^2 +1)^\frac{\alpha}{2}$ and $ \omega_2(x)= g(x) (|x|^2 +1)^\frac{\beta}{2}$,  where $ g(x) $ is positive except at say a point, smooth and where $ \lim_{|x| \rightarrow \infty} g(x) = C \in (0,\infty)$      For this class of weights we can essentially obtain optimal results 


\begin{thm} \label{alpha_beta}   Take $ \omega_1 $ and $ \omega_2$ as above 
\begin{enumerate}

\item If $ N+ \alpha - 2 <0$ then there is no stable sub-solution for $(G)$, $(L)$ (here we require it to be positive) and in the case of $(M)$ there is no positive  stable  super-solution  This case is the trivial case, see Remark \ref{triv}   \\



\textbf{Assumption:} For the remaining cases we assume that $ N + \alpha -2 > 0$ 

  \item If  $N+\alpha-2<4(\beta-\alpha+2)$ then there is no  stable sub-solution for $ (G)$ 

\item If $N+\alpha-2<\frac{ 2(\beta-\alpha+2)   }{p-1} \left( p+\sqrt{p(p-1)}  \right)$ then there is  no positive stable sub-solution of $(L)$ 

\item If $N+\alpha-2<\frac{2(\beta-\alpha+2)   }{p+1} \left( p+\sqrt{p(p+1)}  \right)$ then there is no positive stable super-solution of $(M)$ 

\item Further more 2,3,4 are optimal in the sense if $ N + \alpha -2 > 0$ and the remaining inequality is not satisfied (and in addition we assume we don't have equality in the inequality) then we can find a suitable function $ g(x)$ which satisfies the above properties and a stable sub/super-solution $u$ for the appropriate equation 

\end{enumerate}

\end{thm}

\begin{remark}  Many of the above results can be extended to the case of equality in either the $ N + \alpha - 2 \ge 0$ and also the other inequality which depends on the equation we are  examining   We omit the details because one cannot prove the results in a unified way 

\end{remark}






















In showing that an explicit solution is stable we will need the   weighted Hardy inequality given in \cite{craig} 
\begin{lemma} \label{Har}
Suppose $ E>0$ is a smooth function   Then one has
\[ (\tau-\frac{1}{2})^2 \int E^{2\tau-2} | \nabla E|^2 \phi^2 + (\frac{1}{2}-\tau) \int (-\Delta E) E^{2\tau-1} \phi^2 \le \int E^{2\tau} | \nabla \phi|^2,\] for all $ \phi \in C_c^\infty({\mathbb{R}}^N)$ and $ \tau \in {\mathbb{R}}$ 
\end{lemma}  By picking an appropriate function $E$ this gives,

\begin{cor} \label{Hardy}
For all $ \phi \in C_c^\infty$ and $ t , \alpha \in {\mathbb{R}}$  We have
  \begin{eqnarray*}
\int (1+|x|^2)^\frac{\alpha}{2} |\nabla\phi|^2 &\ge& (t+\frac{\alpha}{2})^2 \int |x|^2 (1+|x|^2)^{-2+\frac{\alpha}{2}}\phi^2\\
&&+(t+\frac{\alpha}{2})\int  (N-2(t+1)   \frac{|x|^2}{1+|x|^2}) (1+|x|^2)^{-1+\frac{\alpha} {2}} \phi^2 
\end{eqnarray*}
 \end{cor}























\section{Proof of main results}

\textbf{ Proof of Theorem \ref{main_non_exist} }   (1)  Suppose $ u$ is a stable sub-solution of $(G)$ with $ I_G,J_G \rightarrow 0$ as $ R \rightarrow \infty$ and  let $ 0 \le \phi \le 1$ denote a smooth compactly supported function   Put $ \psi:= e^{tu} \phi$ into (\ref{stable}), where $ 0 <t<2$, to arrive at
\begin{eqnarray*}
\int \omega_2 e^{(2t+1)u} \phi^2 &\le & t^2 \int \omega_1 e^{2tu} | \nabla u|^2 \phi^2 \\
&& +\int \omega_1 e^{2tu}|\nabla \phi|^2 + 2 t \int \omega_1 e^{2tu} \phi \nabla u \cdot \nabla \phi 
\end{eqnarray*}  Now multiply $(G)$ by $ e^{2tu} \phi^2$ and integrate by parts to arrive at
\[ 2t \int \omega_1 e^{2tu} | \nabla u|^2 \phi^2 \le \int \omega_2 e^{(2t+1) u} \phi^2 - 2 \int \omega_1 e^{2tu} \phi \nabla u \cdot \nabla \phi,\]
and now if one equates like terms they arrive at
\begin{eqnarray} \label{start}
\frac{(2-t)}{2} \int \omega_2 e^{(2t+1) u} \phi^2  & \le & \int \omega_1 e^{2tu} \left( | \nabla \phi |^2 - \frac{ \Delta \phi}{2} \right) dx \nonumber \\
&& - \frac{1}{2} \int e^{2tu} \phi \nabla \omega_1 \cdot \nabla \phi 
\end{eqnarray}   Now substitute $ \phi^m$ into this inequality for $ \phi$ where $ m $ is a big integer to obtain
\begin{eqnarray} \label{start_1}
\frac{(2-t)}{2} \int \omega_2 e^{(2t+1) u} \phi^{2m}  & \le & C_m \int \omega_1 e^{2tu} \phi^{2m-2}  \left( | \nabla \phi |^2 + \phi |\Delta \phi|  \right) dx \nonumber \\
&& - D_m \int e^{2tu} \phi^{2m-1} \nabla \omega_1 \cdot \nabla \phi
\end{eqnarray} where $ C_m$ and $ D_m$ are positive constants just depending on $m$    We now estimate the  terms on the right but we mention that when ones assume the appropriate monotonicity on $ \omega_1$ it is the last integral on the right which one is able to drop 

\begin{eqnarray*}
\int \omega_1 e^{2tu} \phi^{2m-2} | \nabla \phi|^2 & = & \int \omega_2^\frac{2t}{2t+1} e^{2tu} \phi^{2m-2}  \frac{ \omega_1 }{\omega_2^\frac{2t}{2t+1}} | \nabla \phi|^2  \\
& \le &  \left( \int \omega_2 e^{(2t+1) u} \phi^{(2m-2) \frac{(2t+1)}{2t}} dx \right)^\frac{2t}{2t+1}\\ &&\left( \int \frac{ \omega_1 ^{2t+1}}{\omega_2^{2t}} | \nabla \phi |^{2(2t+1)} \right)^\frac{1}{2t+1} 
\end{eqnarray*}
Now, for fixed $ 0 <t<2$ we can take $ m $ big enough so $ (2m-2) \frac{(2t+1)}{2t} \ge 2m $ and since $ 0 \le \phi \le 1$ this allows us to replace the power on $ \phi$ in the first term on the right with $2m$   and hence we obtain
 \begin{equation} \label{three}
 \int \omega_1 e^{2tu} \phi^{2m-2} | \nabla \phi|^2  \le \left( \int \omega_2 e^{(2t+1) u} \phi^{2m} dx \right)^\frac{2t}{2t+1} \left( \int \frac{ \omega_1 ^{2t+1}}{\omega_2^{2t}} | \nabla \phi |^{2(2t+1)} \right)^\frac{1}{2t+1} 
 \end{equation}    We now take the test functions $ \phi$ to be such that $ 0 \le  \phi \le 1$ with $ \phi $ supported in the ball $ B_{2R}$ with $ \phi = 1 $ on $ B_R$ and $ | \nabla \phi | \le \frac{C}{R}$ where $ C>0$ is independent of $ R$    Putting this choice of $ \phi$ we obtain
 \begin{equation} \label{four}
 \int \omega_1 e^{2tu} \phi^{2m-2} | \nabla \phi |^2 \le \left( \int \omega_2 e^{(2t+1)u} \phi^{2m} \right)^\frac{2t}{2t+1} I_G^\frac{1}{2t+1} 
 \end{equation}  One similarly shows that
 \[ \int \omega_1 e^{2tu} \phi^{2m-1} | \Delta \phi| \le \left( \int \omega_2 e^{(2t+1)u} \phi^{2m} \right)^\frac{2t}{2t+1} I_G^\frac{1}{2t+1} \]
 So, combining the results we obtain

 \begin{eqnarray} \label{last} \nonumber \frac{(2-t)}{2} \int \omega_2 e^{(2t+1) u} \phi^{2m} &\le& C_m \left( \int \omega_2 e^{(2t+1) u} \phi^{2m} dx \right)^\frac{2t}{2t+1} I_G^\frac{1}{2t+1}\\
 &&- D_m \int e^{2tu} \phi^{2m-1}  \nabla \omega_1 \cdot \nabla \phi 
 \end{eqnarray}
 We now estimate this last term   A similar argument using H\"{o}lder's inequality shows that
 \[ \int e^{2tu} \phi^{2m-1} | \nabla \omega_1| | \nabla \phi| \le \left(  \int \omega_2 \phi^{2m} e^{(2t+1) u} dx \right)^\frac{2t}{2t+1} J_G^\frac{1}{2t+1}  \] Combining the results gives that
\begin{equation} \label{last}
(2-t) \left( \int \omega_2 e^{(2t+1) u} \phi^{2m} dx \right)^\frac{1}{2t+1} \le I_G^\frac{1}{2t+1} + J_G^\frac{1}{2t+1},
\end{equation} and now we send $ R \rightarrow \infty$ and use the fact that $ I_G, J_G \rightarrow 0$ as $ R \rightarrow \infty$ to see that
\[ \int \omega_2 e^{(2t+1) u} =0, \] which is clearly a contradiction    Hence there is no stable sub-solution of $(G)$ 

(2)    Suppose that $u >0$ is a stable sub-solution (super-solution) of $(L)$   Then a similar calculation as in (1) shows that for  $ p - \sqrt{p(p-1)} <t < p + \sqrt{p(p-1)}$,  $( 0 <t<\frac{1}{2})$ one has

\begin{eqnarray}   \label{shit}
(p  -\frac{t^2}{2t-1}   )\int \omega_2 u^{2t+p-1} \phi^{2m} & \le & D_m \int \omega_1 u^{2t} \phi^{2(m-1)} (|\nabla\phi|^2  +\phi |\Delta \phi |) \nonumber \\
&& +C_m \frac{(1-t)}{2(2t-1)} \int u^{2t} \phi^{2m-1}\nabla \omega_1 \cdot  \nabla \phi 
 \end{eqnarray}    One now applies H\"{o}lder's argument as in (1) but the terms $ I_L$ and $J_L$ will appear on the right hand side of the resulting
 equation       This shift from a sub-solution to a super-solution depending on whether $ t >\frac{1}{2}$ or $ t < \frac{1}{2}$ is a result from the sign change of $ 2t-1$ at $ t = \frac{1}{2}$    We leave the details for the reader 


(3)   This case is also similar to (1) and (2) 


\hfill $ \Box$

 \textbf{Proof of Theorem \ref{mono} }   (1)   Again we suppose there is a stable sub-solution $u$ of $(G)$   Our starting point  is (\ref{start_1}) and we wish to be able to drop the term
 \[ - D_m \int e^{2tu} \phi^{2m-1} \nabla \omega_1 \cdot \nabla \phi, \] from (\ref{start_1})     We can choose $ \phi$ as in the proof of Theorem \ref{main_non_exist} but also such that $ \nabla \phi(x) = - C(x) x$ where $ C(x) \ge 0$      So if we assume that $ \nabla \omega_1 \cdot x \le 0$ for big $x$ then we see that this last term is non-positive and hence we can drop the term   The the proof is as before but now we only require that $ \lim_{R \rightarrow \infty} I_G=0$ 

 (2)      Suppose that $ u >0$ is a stable sub-solution of $(L)$  and so (\ref{shit}) holds for all $  p - \sqrt{p(p-1)} <t< p + \sqrt{p(p-1)}$    Now we wish to use monotonicity to drop the term from (\ref{shit}) involving the term $ \nabla \omega_1 \cdot \nabla \phi$       $ \phi$ is chosen the same as in (1)  but here one notes that the co-efficient for this term changes sign at $ t=1$ and hence by restriction $t$ to the appropriate side of 1 (along with the above condition on $t$ and $\omega_1$) we can drop the last term depending on which monotonicity we have and hence to obtain a contraction we only require that $ \lim_{R \rightarrow \infty} I_L =0$    The result for the non-existence of a stable super-solution is similar be here one restricts $ 0 < t < \frac{1}{2}$ 


(3)   The proof here is similar to (1) and (2) and we omit the details 




 \hfill $\Box$













\textbf{Proof of Corollary \ref{thing} }   We suppose  that $ \omega_1 \le C  \omega_2$  for big $ x$, $ \omega_2 \in L^\infty$,  $ \nabla \omega_1(x) \cdot  x \le 0$ for big $ x$      \\
(1)     Since $ \nabla \omega_1 \cdot x \le 0$ for big $x$ we can apply Theorem \ref{mono} to show the non-existence of a stable solution to $(G)$    Note that  with the above assumptions on $ \omega_i$ we have that
\[ I_G \le \frac{C R^N}{R^{4t+2}} \]  For $ N \le 9$  we can take $ 0 <t<2$  but close enough to $2$ so the right hand side goes to zero as $ R \rightarrow \infty$ 

Both (2) and (3) also follow directly from applying Theorem \ref{mono}    Note that one can say more about (2) by taking the multiple cases as listed in Theorem \ref{mono} but we have choice to leave this to the reader 


\hfill $ \Box$


\textbf{Proof of Corollary \ref{po} }   Since we have no monotonicity conditions now we will need both $I$ and $J$ to go to zero to show the non-existence of a stable solution    Again the results are obtained immediately by applying Theorem \ref{main_non_exist}  and we prefer to omit the details 


\hfill $\Box$
















\textbf{Proof of Theorem \ref{alpha_beta} }  (1)   If $ N + \alpha -2 <0$ then using Remark \ref{triv}  one easily sees there is no stable sub-solution of $(G)$ and $(L)$ (positive for $(L)$) or a positive stable super-solution of $(M)$    So we now assume that $ N + \alpha -2 > 0$      Note that the monotonicity of $ \omega_1$ changes when $ \alpha $ changes sign and hence one would think that we need to consider separate cases if we hope to utilize the monotonicity results    But a computation shows that in fact $ I$ and $J$ are just multiples of each other in all three cases so it suffices to show, say, that $ \lim_{R \rightarrow \infty} I =0$  \\
(2)    Note that for $ R >1$ one has
\begin{eqnarray*}
I_G & \le  & \frac{C}{R^{4t+2}} \int_{R <|x| < 2R} |x|^{ \alpha (2t+1) - 2t \beta} \\
& \le  &  \frac{C}{R^{4t+2}}  R^{N + \alpha (2t+1) - 2t \beta},
\end{eqnarray*} and so to show the non-existence we want to find some $ 0 <t<2$ such that
$  4t+2 > N  + \alpha(2t+1) - 2 t \beta$,   which is equivalent to  $ 2t ( \beta - \alpha +2) > (N + \alpha -2)$     Now recall that we are assuming that $ 0 < N + \alpha -2 < 4 ( \beta - \alpha +2) $ and hence we have the desired result by taking $  t <2$ but sufficiently close 
The proof of the non-existence results for
(3) and (4) are similar and we omit the details    \\
(5)   We now assume that $N+\alpha-2>0$   In showing the existence of stable sub/super-solutions we need to consider  $ \beta - \alpha + 2 <0$ and $ \beta - \alpha +2 >0$ separately 

\begin{itemize} \item $(\beta - \alpha + 2 <0)$  Here we take $ u(x)=0$ in the case of $(G)$ and $ u=1$ in the case of $(L)$ and $(M)$  In addition we take $ g(x)=\E$   It is clear that in all cases $u$ is the appropriate sub or super-solution   The only thing one needs to check is the stability     In all cases this reduces to trying to show that we have
\[ \sigma \int       (1+|x|^2)^{\frac{\alpha}{2}  -1}    \phi^2 \le \int      (1+|x|^2)^{\frac{\alpha}{2}}   | \nabla\phi  |^2,\]  for all $ \phi \in C_c^\infty$ where  $ \sigma $ is some small positive constant; its either $ \E$ or $ p \E$ depending on which equation were are examining 
To show this we use the result from Corollary \ref{Hardy} and we drop a few positive terms to arrive at
\begin{equation*}
\int (1+|x|^2)^\frac{\alpha}{2} |\nabla\phi|^2\ge (t+\frac{\alpha}{2})\int \left (N-2(t+1)   \frac{|x|^2}{1+|x|^2}\right) (1+|x|^2)^{-1+\frac{\alpha} {2}}
\end{equation*} which holds for all $ \phi \in C_c^\infty$ and $ t,\alpha \in {\mathbb{R}}$ 
  Now, since $N+\alpha-2>0$, we can choose $t$ such that $-\frac{\alpha}{2}<t<\frac{n-2}{2}$   So, the integrand function in the right hand side is positive and since for small enough $\sigma$ we have
  \begin{equation*}
\sigma \le  (t+\frac{\alpha}{2})(N-2(t+1)   \frac{|x|^2}{1+|x|^2})  \ \ \ \text {for all} \ \ x\in \mathbb{R}^N
\end{equation*}
 we get stability 



\item ($\beta-\alpha+2>0$) In the case of $(G)$ we take   $u(x)=-\frac{\beta-\alpha+2}{2} \ln(1+|x|^2)$ and $g(x):= (\beta-\alpha+2)(N+(\alpha-2)\frac{|x|^2}{1+|x|^2})$  By a computation one sees that $u$ is a sub-solution of $(G)$ and hence we need now to only show the stability, which amounts to showing that
\begin{equation*}
\int \frac{g(x)\psi^2}{(1+|x|^{2   })^{-\frac{\alpha}{2}+1}}\le \int\frac{|\nabla\psi|^2}{    (1+|x|^2)^{-\frac{\alpha}{2}}     },
\end{equation*} for all $ \psi \in C_c^\infty$   To show this we use  Corollary \ref{Hardy}   So  we  need to choose an appropriate $t$ in   $-\frac{\alpha}{2}\le t\le\frac{N-2}{2}$  such that for all $x\in {\mathbb{R}}^N$ we have
 \begin{eqnarray*}
 (\beta-\alpha+2)\left(    N+  (\alpha-2)\frac{|x|^2}{1+|x|^2}\right)         &\le& (t+\frac{\alpha}{2})^2 \frac{ |x|^2 }{(1+|x|^2}\\
&&+(t+\frac{\alpha}{2}) \left(N-2(t+1)   \frac{|x|^2}{1+|x|^2}\right) 
\end{eqnarray*}
With a  simple calculation one sees we need just to have
   \begin{eqnarray*}
 (\beta-\alpha+2)&\le& (t+\frac{\alpha}{2}) \\
  (\beta-\alpha+2)     \left(    N+  \alpha-2\right)      &   \le&  (t+\frac{\alpha}{2}) \left(N-t-2+\frac{\alpha}{2}) \right) 
 \end{eqnarray*}     If one takes $ t= \frac{N-2}{2}$ in the case where $ N \neq 2$ and $ t $ close to zero in the case for $ N=2$ one easily sees the above inequalities both hold, after considering all the constraints on $ \alpha,\beta$ and $N$ 

 We now consider the case of $(L)$   Here one takes $g(x):=\frac {\beta-\alpha+2}{p-1}(    N+  (\alpha-2-\frac{\beta-\alpha+2}{p-1})
\frac{|x|^2}{1+|x|^2})$ and $ u(x)=(1+|x|^2)^{ -\frac  {\beta-\alpha+2}{2(p-1)} }$    Using essentially the same approach as in $(G)$ one shows that $u$ is a stable sub-solution of $(L)$ with this choice of $g$    \\
For the case of $(M)$ we take   $u(x)=(1+|x|^2)^{ \frac  {\beta-\alpha+2}{2(p+1)}   }$ and $g(x):=\frac {\beta-\alpha+2}{p+1}(    N+  (\alpha-2+\frac{\beta-\alpha+2}{p+1})
\frac{|x|^2}{1+|x|^2})$ 



\end{itemize}


\hfill  $  \Box$









Context 3: Paper Info

Title: Interpretable reduced-order modeling with time-scale separation Interpretable reduced-order modeling with time-scale separation
Publish Date: 7 March 2023
Author List: Sebastian Kaltenbach (from CSE-Lab, ETH Zurich, Harvard SEAS), Phaedon-Stelios Koutsourelakis (from CSE-Lab, ETH Zurich, Harvard SEAS), Petros Koumoutsakos (from CSE-Lab, ETH Zurich, Harvard SEAS), Harvard Seas (from CSE-Lab, ETH Zurich, Harvard SEAS)

Figure

FIG  5  Comparison between the phase-space of the reference solution (left) and the phase-space of the predictions
FIG  7  Comparison between predictions and reference solutions for a new initial condition fort = 1 25, 3 75, 7 5, 12 5, 20, 30  (from left to right and top to down) We note that with longer prediction time the uncertainty bounds increases Despite the chaotic nature of the KS equation, the predictive posterior mean is close to the reference solution for t ≤ 12 5

abstract

Partial Differential Equations (PDEs) with high dimensionality are commonly encountered in computational physics and engineering  However, finding solutions for these PDEs can be computationally expensive, making model-order reduction crucial  We propose such a data-driven scheme that automates the identification of the time-scales involved and, can produce stable predictions forward in time as well as under different initial conditions not included in the training data 
To this end, we combine a non-linear autoencoder architecture with a time-continuous model for the latent dynamics in the complex space  It readily allows for the inclusion of sparse and irregularly sampled training data  The learned, latent dynamics are interpretable and reveal the different temporal scales involved 
We show that this data-driven scheme can automatically learn the independent processes that decompose a system of linear ODEs along the eigenvectors of the system's matrix  Apart from this, we demonstrate the applicability of the proposed framework in a hidden Markov Model and the (discretized) Kuramoto-Shivashinsky (KS) equation 
Additionally, we propose a probabilistic version, which captures predictive uncertainties and further improves upon the results of the deterministic framework 

INTRODUCTION

High-fidelity simulations of critical phenomena such as ocean dynamics and epidemics have become essential for decision-making  They are based on physically-motivated PDEs expressing system dynamics that span multiple spatiotemporal scales and which necessitate cumbersome computations   In recent years there is increased attention to the development of data-driven models that can accelerate the solution of these PDEs as well as reveal salient, lower-dimensional features that control the long-term evolution 
In most cases, data-driven reduced-order models are not interpretable  In particular, models based on neural networks despite good predictive capabilities , they offer a black-box description of the system dynamics  A possible remedy is applying a symbolic regression to the learned neural network representation , but this adds additional computational cost due to the two-step procedure 
A number of frameworks such as SINDy allows to learn interpretable dynamics but it relies on the a-priori availability of lower-dimensional descriptors and of time-derivatives which can be very noisy for both simulation and experimental data  Other frameworks are tailored to specific problems such as molecular dynamics  
Here, we present a framework that only needs the value of the observables, and not their derivatives, as training data and is capable of identifying interpretable latent dynamics  The deployment of interpretable latent dynamics ensures that conservation of important properties of that are reflected in the reduced-order model  
The present method is related to approaches based on the Koopman-operator extended Dynamic Mode Decomposition (eDMD) but uses continuous complex-valued latent space dynamics and only requires one scalar variable per latent dimension to describe the latent space dynamics  Therefore we do not have to enforce any parametrizations on the Koopman matrix  
The time-continuous formulation moreover allows to incorporate sparse and irregularly sampled training data and fast generation of predictions after the training phase  By using a complex-valued latent space we can also incorporate harmonic effects and reduce the number of latent variables needed  Linear and non-linear autoencoders are used to map the observed, high-dimensional time-series to the lower-dimensional, latent representation and we identify simultaneously the autoencoder as well as the latent dynamics by optimizing a combined loss function 
Hence the to tasks of dimensionality reduction and discovery of the reduced dynamics are unified while other frameworks treat the two parts separately   Apart from using an architecture based on autoencoders to identify the latent space, projection-based methods could also be employed   We are also proposing a probabilistic version of our algorithm ) that makes use of probabilistic Slow Feature Analysis  
This allows for a latent representation that arart from being time-continuous, can quantify the predictive uncertainty and hierarchically decompose the dynamics into their pertinent scales while promoting the discovery of slow processes that control the system's evolution over long time horizons  The rest of the paper is structured as follows: We introduce the methodological framework as well as algorithmic details in section II 
Particular focus is paid on the interpretability of the inferred lower-dimensional dynamics  In section III we present three numerical illustrations, i e  a system of linear ODEs, a hidden Markov Model and the discretized KS-equation  We then present in section IV the probabilistic extension of the framework and apply it to the KS-equation 
We conclude with a summary and a short discussion about possible next steps  We introduce the autoencoders deployed in this work, followed by the interpretable latent space dynamic and discuss the training process  We consider data from high-dimensional time series x n ∈ R f with n = 1,    , T   We remark that the intervals between the different states do not need to be uniformly spaced 

Autoencoder

A core assumption of the method is that each high-dimensional state x n can be compressed to a lower-dimensional representation z n ∈ C c with c << f   We identify this lower-dimensional representation by an autoencoder consisiting of a parameterized encoder and decoder  The encoder maps the high-dimensional representation to the latent space as:
The latent space is complex-valued  The decoder reconstructs the high-dimensional representation based on the latent variables as: We denote the parameters of the encoder as well as the decoder by θ  As discussed later in Section II C, both set of parameters are optimized simultaneously during training and therefore there is no need for differentiating them 

Interpretable Latent Space Dynamics

We employ a propagator in the latent space to capture the reduced-order dynamics of the system  In contrast to other time-extended variational autoencoder frameworks, our representation uses complex valued latent variables  In addition the latent variables are treated independently  The latter feature enables us to have an interpretable latent dynamics as well as a model that is especially suitable for being trained in the Small Data regime due to the small number of required parameters 
This is in contrast to temporal propagators such as LSTMs   For each dimension i of the latent variable z we are using the following continuous ODE in the complex plane: By solving this ODE, we can define the operator: Interpretable reduced-order modeling with time-scale separation Here, λ is a vector containing all the individual λ's and ∆t n indicates the time-step between the latent states 
The symbol is used to indicate a component-wise multiplication  We remark that the latent variables and the parameter governing the temporal evolution are complex numbers and their role in describing the system dynamics is similar to that of an eigenvalue  The real part is associated with growth and decay whereas the imaginary part is representing the periodic component 
This approach has similarities with the Koopman-operator based methods and the extended dynamic mode decomposition   In contrast to the methods mentioned before we are using a continuous formulation in the latent space that allows us to incorporate scarce and irregularly sampled training data and directly rely on complex numbers in the latent space 

Training and Predictions

We optimize a loss function that combines both a reconstruction loss as well as a loss associated with the error of our learned propagator in the latent space: (5) We note that we could directly incorporate mini-batch training by only taking the summation over a subset of the N available training data 
For new predictions of unseen states, we use the encoder to generate a latent representations which is then advanced in time by the learned propagator  At a designated time step we are using the decoder to reconstruct the high-dimensional solution  We applied our algorithm to three systems  First, we show that the algorithm is capable of exactly reproducing the solution of a linear ODE and to identify its eigenvalues 
Afterwards we are applying the framework to a high-dimensional process generated by a complex latent dynamics, which is correctly identified  As a final test case, we are applying the algorithm to a Kuramoto Shivashinski (KS) equation  Interpretable reduced-order modeling with time-scale separation

Linear ODE

We are considering a two-dimensional ODE system for x = y 1 y 2 : Based on the obtained training data we run our algorithm using a linear encoder and decoder structure as well as two latent variables z



ground_truth: Sir Richard.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What are the stability conditions for a solution of $-\Delta u = f(u)$?

Read the following text and answer briefly.

Context 4: \section{Introduction}
\label{sec:introduction}

Probabilistic models have proven to be very useful in a lot of applications in signal processing where signal estimation is needed \cite{rabiner1989tutorial,arulampalam2002tutorial,ji2008bayesian}  Some of their advantages are that 1) they force the designer to specify all the assumptions of the model, 2) they provide a clear separation between the model and the algorithm used to solve it, and 3) they usually provide some measure of uncertainty about the estimation 

On the other hand, adaptive filtering is a standard approach in estimation problems when the input is received as a stream of data that is potentially non-stationary  This approach is widely understood and applied to several problems such as echo cancellation \cite{gilloire1992adaptive}, noise cancellation \cite{nelson1991active}, and channel equalization \cite{falconer2002frequency} 

Although these two approaches share some underlying relations, there are very few connections in the literature  The first important attempt in the signal processing community to relate these two fields was the connection between a linear Gaussian state-space model (i e  Kalman filter) and the RLS filter, by Sayed and Kailath \cite{sayed1994state} and then by Haykin \emph{et al } \cite{haykin1997adaptive}  The RLS adaptive filtering algorithm emerges naturally when one defines a particular state-space model (SSM) and then performs exact inference in that model  This approach was later exploited in \cite{van2012kernel} to design a kernel RLS algorithm based on Gaussian processes 

A first attempt to approximate the LMS filter from a probabilistic perspective was presented in \cite{park2014probabilistic}, focusing on a kernel-based implementation  The algorithm of \cite{park2014probabilistic} makes use of a Maximum a Posteriori (MAP) estimate as an approximation for the predictive step  However, this approximation does not preserve the estimate of the uncertainty in each step, therefore degrading the performance of the algorithm 

In this work, we provide a similar connection between state-space models and least-mean-squares (LMS)  Our approach is based on approximating the posterior distribution with an isotropic Gaussian distribution  We show how the computation of this approximated posterior leads to a linear-complexity algorithm, comparable to the standard LMS  Similar approaches have already been developed for a variety of problems such as channel equalization using recurrent RBF neural networks \cite{cid1994recurrent}, or Bayesian forecasting \cite{harrison1999bayesian}  Here, we show the usefulness of this probabilistic approach for adaptive filtering 

The probabilistic perspective we adopt throughout this work presents two main advantages  Firstly, a novel LMS algorithm with adaptable step size emerges naturally with this approach, making it suitable for both stationary and non-stationary environments  The proposed algorithm has less free parameters than previous LMS algorithms with variable step size \cite{kwong1992variable,aboulnasr1997robust,shin2004variable}, and its parameters are easier to be tuned w r t  these algorithms and standard LMS  Secondly, the use of a probabilistic model provides us with an estimate of the error variance, which is useful in many applications 

Experiments with simulated and real data show the advantages of the presented approach with respect to previous works  However, we remark that the main contribution of this paper is that it opens the door to introduce more Bayesian machine learning techniques, such as variational inference and Monte Carlo sampling methods \cite{barber2012bayesian}, to adaptive filtering \\


\section{Probabilistic Model}

Throughout this work, we assume the observation model to be linear-Gaussian with the following distribution,

\begin{equation}
p(y_k|{\bf w}_k) = \mathcal{N}(y_k;{\bf x}_k^T {\bf w}_k , \sigma_n^2),
\label{eq:mess_eq}
\end{equation}
where  $\sigma_n^2$ is the variance of the observation noise, ${\bf x}_k$ is the regression vector and ${\bf w}_k$ is the parameter vector to be sequentially estimated, both $M$-dimensional column vectors 


In a non-stationary scenario, ${\bf w}_k$ follows a dynamic process  In particular, we consider a diffusion process (random-walk model) with variance $\sigma_d^2$ for this parameter vector:


\begin{equation}
p({\bf w}_k|{\bf w}_{k-1})= \mathcal{N}({\bf w}_k;{\bf w}_{k-1}, \sigma_d^2 {\bf I}),
\label{eq:trans_eq}
\end{equation}
where $\bf I$ denotes the identity matrix  In order to initiate the recursion, we assume the following prior distribution on ${\bf w}_k$

\begin{equation}
p({\bf w}_0)= \mathcal{N}({\bf w}_0;0, \sigma_d^2{\bf I}) \nonumber
\end{equation}

\section{Exact inference in this model: Revisiting the RLS filter}

Given the described probabilistic SSM, we would like to infer the posterior probability distribution $p({\bf w}_k|y_{1:k})$ 
Since all involved distributions are Gaussian, one can perform exact inference, leveraging the probability rules in a straightforward manner  The resulting probability distribution is
\begin{equation}
p({\bf w}_k|y_{1:k}) =  \mathcal{N}({\bf w}_k;{\bf\boldsymbol\mu}_{k}, \boldsymbol\Sigma_{k}), \nonumber
\end{equation}
in which the mean vector ${\bf\boldsymbol\mu}_{k}$ is given by
\begin{equation}
{\bf\boldsymbol\mu}_k = {\bf\boldsymbol\mu}_{k-1} + {\bf K}_k (y_k - {\bf x}_k^T {\bf\boldsymbol\mu}_{k-1}){\bf x}_k, \nonumber
\end{equation}
where we have introduced the auxiliary variable
\begin{equation}
{\bf K}_k = \frac{ \left(\boldsymbol\Sigma_{k-1} + \sigma_d^2 {\bf I}\right)}{{\bf x}_k^T  \left(\boldsymbol\Sigma_{k-1} + \sigma_d^2 {\bf I}\right)  {\bf x}_k + \sigma_n^2}, \nonumber
\end{equation}
and the covariance matrix $\boldsymbol\Sigma_k$ is obtained as
\begin{equation}
\boldsymbol\Sigma_k = \left( {\bf I} -  {\bf K}_k{\bf x}_k {\bf x}_k^T \right) ( \boldsymbol\Sigma_{k-1} +\sigma_d^2), \nonumber
\end{equation}
Note that the mode of $p({\bf w}_k|y_{1:k})$, i e  the maximum-a-posteriori estimate (MAP), coincides with the RLS adaptive rule
\begin{equation}
{{\bf w}}_k^{(RLS)} = {{\bf w}}_{k-1}^{(RLS)} + {\bf K}_k (y_k - {\bf x}_k^T {{\bf w}}_{k-1}^{(RLS)}){\bf x}_k  
\label{eq:prob_rls}
\end{equation}
This rule is similar to the one introduced in \cite{haykin1997adaptive} 

Finally, note that the covariance matrix $\boldsymbol\Sigma_k$ is a measure of the uncertainty of the estimate ${\bf w}_k$ conditioned on the observed data $y_{1:k}$  Nevertheless, for many applications a single scalar summarizing the variance of the estimate could prove to be sufficiently useful  In the next section, we show how such a scalar is obtained naturally when $p({\bf w}_k|y_{1:k})$ is approximated with an isotropic Gaussian distribution  We also show that this approximation leads to an LMS-like estimation 
 


\section{Approximating the posterior distribution: LMS filter }

The proposed approach consists in approximating the posterior distribution $p({\bf w}_k|y_{1:k})$, in general a multivariate Gaussian distribution with a full covariance matrix, by an isotropic spherical Gaussian distribution 

\begin{equation}
\label{eq:aprox_post}
\hat{p}({\bf w}_{k}|y_{1:k})=\mathcal{N}({\bf w}_{k};{\bf \hat{\boldsymbol\mu}}_{k}, \hat{\sigma}_{k}^2 {\bf I} ) 
\end{equation}

In order to estimate the mean and covariance of the approximate distribution $\hat{p}({\bf w}_{k}|y_{1:k})$, we propose to select those that minimize the Kullback-Leibler divergence with respect to the original distribution, i e , 

\begin{equation}
\{\hat{\boldsymbol\mu}_k,\hat{\sigma}_k\}=\arg \displaystyle{  \min_{\hat{\boldsymbol\mu}_k,\hat{\sigma}_k}} \{ D_{KL}\left(p({\bf w}_{k}|y_{1:k}))\| \hat{p}({\bf w}_{k}|y_{1:k})\right) \}  \nonumber
\end{equation}

The derivation of the corresponding minimization problem can be found in Appendix A  In particular, the optimal mean and the covariance are found as
\begin{equation}
{\hat{\boldsymbol\mu}}_{k} = {\boldsymbol\mu}_{k};~~~~~~ \hat{\sigma}_{k}^2 = \frac{{\sf Tr}\{ \boldsymbol\Sigma_k\} }{M} 
\label{eq:sigma_hat}
\end{equation}


We now show that by using \eqref{eq:aprox_post} in the recursive predictive and filtering expressions we obtain an LMS-like adaptive rule  First, let us assume that we have an approximate posterior distribution at $k-1$, $\hat{p}({\bf w}_{k-1}|y_{1:k-1}) =  \mathcal{N}({\bf w}_{k-1};\hat{\bf\boldsymbol\mu}_{k-1}, \hat{\sigma}_{k-1}^2 {\bf I} )$  Since all involved distributions are Gaussian, the predictive distribution
is obtained as %
\begin{eqnarray}
\hat{p}({\bf w}_k|y_{1:k-1}) &=& \int p({\bf w}_k|{\bf w}_{k-1}) \hat{p}({\bf w}_{k-1}|y_{1:k-1}) d{\bf w}_{k-1} \nonumber\\
&=& \mathcal{N}({\bf w}_k;{\bf\boldsymbol\mu}_{k|k-1}, \boldsymbol\Sigma_{k|k-1}), 
\label{eq:approx_pred}
\end{eqnarray}
where the mean vector and covariance matrix are given by
\begin{eqnarray}
\hat{\bf\boldsymbol\mu}_{k|k-1} &=& \hat{\bf\boldsymbol\mu}_{k-1} \nonumber \\
\hat{\boldsymbol\Sigma}_{k|k-1} &=& (\hat{\sigma}_{k-1}^2 + \sigma_d^2 ){\bf I}\nonumber 
\end{eqnarray}

From \eqref{eq:approx_pred}, the posterior distribution at time $k$ can be computed using Bayes' Theorem and standard Gaussian manipulations (see for instance \cite[Ch  4]{murphy2012machine})  Then, we approximate the posterior $p({\bf w}_k|y_{1:k})$ with an isotropic Gaussian,
\begin{equation}
\hat{p}({\bf w}_k|y_{1:k}) =  \mathcal{N}({\bf w}_k ; {\hat{\boldsymbol\mu}}_{k}, \hat{\sigma}_k^2 {\bf I} ),\nonumber
\end{equation}
where 
\begin{eqnarray}
{\hat{\boldsymbol\mu}}_{k} &= & {\hat{\boldsymbol\mu}}_{k-1}+ \frac{ (\hat{\sigma}_{k-1}^2+ \sigma_d^2)  }{(\hat{\sigma}_{k-1}^2+ \sigma_d^2)  \|{\bf x}_k\|^2 + \sigma_n^2} (y_k - {\bf x}_k^T {\hat{\boldsymbol\mu}}_{k-1}){\bf x}_k  \nonumber  \\
&=& {\hat{\boldsymbol\mu}}_{k-1}+ \eta_k (y_k - {\bf x}_k^T {\hat{\boldsymbol\mu}}_{k-1}){\bf x}_k   
\label{eq:prob_lms}
\end{eqnarray}
Note that, instead of a gain matrix ${\bf K}_k$ as in Eq ~\eqref{eq:prob_rls}, we now have a scalar gain $\eta_k$ that operates as a variable step size 


Finally, to obtain the posterior variance, which is our measure of uncertainty, we apply \eqref{eq:sigma_hat} and the trick ${\sf Tr}\{{\bf x}_k{\bf x}_k^T\}= {\bf x}_k^T{\bf x}_k= \|{\bf x}_k \|^2$,

\begin{eqnarray}
\hat{\sigma}_k^2 &=& \frac{{\sf Tr}(\boldsymbol\Sigma_k)}{M} \\
&=& \frac{1}{M}{\sf Tr}\left\{ \left( {\bf I} -  \eta_k {\bf x}_k {\bf x}_k^T \right) (\hat{\sigma}_{k-1}^2 +\sigma_d^2)\right\} \\
&=& \left(1 - \frac{\eta_k \|{\bf x}_k\|^2}{M}\right)(\hat{\sigma}_{k-1}^2 +\sigma_d^2) 
\label{eq:sig_k}
\end{eqnarray}
If MAP estimation is performed, we obtain  an adaptable step-size LMS estimation

\begin{equation}
{\bf w}_{k}^{(LMS)} = {\bf w}_{k-1}^{(LMS)} + \eta_k (y_k - {\bf x}_k^T {\bf w}_{k-1}^{(LMS)}){\bf x}_k, 	
\label{eq:lms}
\end{equation}
with
\begin{equation}
\eta_k = \frac{ (\hat{\sigma}_{k-1}^2+ \sigma_d^2)  }{(\hat{\sigma}_{k-1}^2+ \sigma_d^2)  \|{\bf x}_k\|^2 + \sigma_n^2} \nonumber
\end{equation}
At this point, several interesting remarks can be made:

\begin{itemize}

\item The adaptive rule \eqref{eq:lms} has linear complexity since it does not require us to compute the full matrix $\boldsymbol\Sigma_k$ 

\item For a stationary model, we have $\sigma_d^2=0$ in \eqref{eq:prob_lms} and \eqref{eq:sig_k}  In this case, the algorithm remains valid and both the step size and the error variance, $\hat{\sigma}_{k}$, vanish over time $k$  

\item Finally, the proposed adaptable step-size LMS has only two parameters, $\sigma_d^2$ and $\sigma_n^2$, (and only one, $\sigma_n^2$, in stationary scenarios) in contrast to other variable step-size algorithms \cite{kwong1992variable,aboulnasr1997robust,shin2004variable}  More interestingly, both $\sigma_d^2$ and $\sigma_n^2$ have a clear underlying physical meaning, and they can be estimated in many cases  We will comment more about this in the next section  
\end{itemize}



\section{Experiments}
\label{sec:experiments}

We evaluate the performance of the proposed algorithm in both stationary and tracking experiments  In the first experiment, we estimate a fixed vector ${\bf w}^{o}$ of dimension $M=50$  The entries of the vector are independently and uniformly chosen in the range $[-1,1]$  Then, the vector is normalized so that $\|{\bf w}^o\|=1$  Regressors $\boldsymbol{x}_{k}$ are zero-mean Gaussian vectors with identity covariance matrix  The additive noise variance is such that the SNR is $20$ dB



ground_truth: $\int f'(u) \psi^2 \le \int | \nabla \psi|^2, \forall \psi \in C_c^2$.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: When was the paper published?

Read the following text and answer briefly.

Context 3:  The loss function was optimized using the Adam algorithm   As we consider a linear ODE we can analytically compute the eigenvalues involved and compare it with the parameters λ identified by our algorithm 
We observe in Figure that the algorithm was able to recover the correct values, i e  the eigenvalues 7 and 3 of the given linear ODE  The system does not have a periodic component and the two imaginary parts correctly go to zero, whereas the real parts converge to the reference value  Moreover we are also able to identify for the linear mapping between our latent variables z and the training data a matrix consisting of a multiple of the eigenvectors (1,1) and (1,-1) and thus the correct solution 
This example was chosen to show that the algorithm is able to quickly identify the exact solution of a linear ODE in terms of its linearly independent components 

Hidden multiscale dynamics

We consider eight-dimensional synthetic time series data produced by an underlying twodimensional complex valued process  In particular, the data points x are generated by first solving for the temporal evolution for the two complex-valued processes p 1 and p 2 and than mapping to the eight-dimensional space by using a randomly sampled linear mapping W  
One of the two processes used to generate the data is chosen to be much slower than the other one and both processes have a periodic component  dp 2 dt = (−0 9 + 1 5i)p 2 (8) As training data we consider 40 time series with 150 data points each, obtained by simulating the described processes for a maximum of t = 15 s and then sampling from the obtained data points 
Hence the training data consists of: • 40 time-series • with each consisting 150 observations of the x at a uniform time-step ∆t = 0 0025 The autoencoder obtained consists of one linear layer for both the decoder as well as the encoder  The model is trained for 5000 iterations using the Adam optimizer and a learning rate of 10 −3  
The results for the convergence of the parameters λ 1 and λ 2 can be found in Figure   We note that the process which is slower decaying and thus more responsible for the long-term evolution of the system has a higher convergence rate than the faster process  With the obtained parameters λ as well as the trained autoencoder, we compute predictions based on the last time step used for training, i e  we apply the encoder to obtain our latent representation and than use the latent dynamics to advance the latent representation in time 
Afterwards, we employ the decoder to reconstruct the full high-dimensional system  The results can be found in Figure and show very good agreement between predictions and reference data  This example shows that our model is successfully able to carry out dimensionality reduction and moreover indicates that the convergence rate between latent processes can be different 
The latter is relevant when training models as for accurate predictions all latent processes and their dynamics should be converged 

Kuramoto-Sivashinsky

Finally, we applied our algorithm to the KS equation and aim to identify a reduced-order model for the solution u(y, t): We employed periodic boundary conditions, µ = 1 and a domain size y ∈ [0, 22]  For this domain-size, the KS-equation exhibits a structurally stable chaotic attractor as discussed in The black lines divides the area for which training data was given from the area without raining data 
;   The equation is discretized in space using a discretization step of 22 64 resulting in a state vector x of dimension 64 and a nonlinear system of coupled ODEs  This is solved using a stiff fourth-order solver  We employed a non-linear encoder and decoder with four fully-connected layers each and ReLU-activation functions as well as Dropout Layers between the fully-connected layers 
We trained the model for 200000 iterations using Adam and a learning rate of 5 • 10 4 and assuming a five-dimensional latent space  We obtained the λ's in Figure   Four latent variables have λ's close to zero and thus a slow temporal dynamic that is responsible for the long-term evolution whereas one latent variable is quickly decaying 
Based on the obtained parameters, we do predictions based on an unseen initial condition not contained in the training data  We are able to reconstruct the correct phase space based on our predictions despite only using a very limited amount of training data  The results for the phase space can be seen in Figure  
Although the small-scale fluctuations in the temporal dynamics are not well captured, the model identifies the correct manifold which has a good accuracy compared to the reference solution  All phase-spaces were obtained by using a finite-difference operator on the data or predictions  These results are in accordance Interpretable reduced-order modeling with time-scale separation with whose LSTM-based temporal dynamic model was also able to find the correct phase space but not to track the actual dynamics for long-term predictions 
Our model is not able to account for noise in the temporal evolution and thus dealing with chaotic, small-scale fluctuations is challenging  We believe that a probabilistic version of our algorithm could be advantageous here  This section contains a fully probabilistic formulation for the deterministic model discussed before 
We replace the Autoencoder with a Variational Autoencoder and the ODE in the latent space with a SDE  The loss function which we optimize is the Evidence-Lower Bound (ELBO) 

Model Structure

We postulate the following relations for our probabilistic model using an Ornstein-Uhlenbeck (OU) for each dimension i of the latent space and a Wiener process W t in the latent space: We again assume that the latent variables z t are complex-valued and a priori independent  Complex variables were chosen as their evolution includes a harmonic components which are observed in many physical systems 
We assume an initial conditions z 0,i ∼ CN (0, σ 2 0,i )  The total parameters associated with the latent space dynamics of our model are thus {σ 2 0,i , σ 2 i , λ i } c i=1 and will be denoted by θ together with all parameters responsible for the decoder mapping G (see next section)  These parameters along with the state variables z t have to be inferred from the data x t  
Based on probabilistic Slow Feature Analysis (SFA) , we set σ 2 i = 2; (λ j ) and σ 2 0,i = 1  As a consequence, a priori, the latent dynamics are stationary  A derivation and reasoning for this choice can be found in Appendix A  Hence the only independent parameters are the λ i , the imaginary part of which can account for periodic effects in the latent dynamics 

Variational Autoencoder

We employ a variational autoencoder to account for a probabilistic mappings from the lower-dimensional representation z n to the high-dimensional system x n   In particular we are employing a probabilistic decoder The encoder is used to infer the state variables z based on the given data and thus defined in the inference and learning section 

Inference and Learning

Given the probabilistic relations , our goal is to infer the latent variables z 0:T as well as all model parameters θ  We follow a hybrid Bayesian approach in which the posterior of the state variables is approximated using amortized Variational Inference and Maximum-A-Posteriori (MAP) point-estimates for θ are computed 
The application of Bayes' rule for each data sequence x 0:T leads to the following posterior: where p(θ) denotes the prior on the model parameters  In the context of variational inference, we use the following factorization of the approximate posterior i e  we infer only the mean µ and variance σ for each state variable based on the given data points 
This conditional density used for inference is the encoder-counterpart to the probabilistic decoder defined in the section before  It can be readily shown that the optimal parameter values are found by maximizing the Evidence Lower Bound (ELBO) F(q φ (z 0:T ), θ) which is derived in Appendix B  We compute Monte Carlo estimates of the gradient of the ELBO with respect to φ and θ with the help of the reparametrization trick and carry out stochastic optimization with the ADAM algorithm  

Results for the probabilistic extension

We applied our probabilistic version to the KS-equation  We used the same settings as for the deterministic approach but considered up to 10 complex latent variables  The obtained λ's are in Figure   The probabilistic model allows us to quantify the uncertainty in predictions  In Figure predictions for various time-steps and the respective uncertainty bounds are shown for an unseen initial condition 
Due to the chaotic nature of the KS-equation and the small amount of training data, the underlying linear dynamic of our model is only able to capture the full dynamics for a limited time horizon  Fortunately, due to the probabilistic approach the model is capable of capturing chaotic fluctuations with increasingly wide uncertainty bounds 
We also computed the phase space representation for the KS-equation based on the predictions obtained by our model and compare it with the reference solution  The probabilistic model identifies the correct manifold with a better accuracy than the deterministic model  As some of the small-scale fluctuations are accounted as noise, the resulting manifold is more concentrated at the origin and the obtained values are slightly smaller than the reference manifold although their shape is very similar 



ground_truth: The paper was published on 7 March 2023.