March 19th
13:50-14:00 Opening Remarks
14:00-15:00 Tutorial 1
Takashi Matsubara (Osaka University)
Deep Geometric Mechanics: From Hamiltonian Neural Networks to Discrete-Time Physics and Beyond

15:00-15:40 Oral Session 1
Oded Ovadia (Tel Aviv University), Vivek Oommen (Brown University), Adar Kahana (BrownUniversity), Ahmad Peyvan (Brown University), Eli Turkel (Tel Aviv University), George Em
Karniadakis (Brown University)
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator(DiTTO)
Imran Nasim (IBM), Melanie Webber (Harvard University)
Learning Reduced Order Dynamics via Geometric Representations

15:40-16:00 Coffee Break

16:00-17:00 Keynote Talk 1
Christopher J. Budd (Bath University)
Adaptivity and Expressivity in Neural Network Approximations and PINNs

March 20th
9:30-10:30 Keynote Talk 2
Christopher Rackauckas (Massachusetts Institute of Technology)
SciML: Adding Scientific Models as Structure to Improve Machine Learning10:40-11:20 Lightning Talk/Video
11:30-12:30 Poster Session 1
Imran Nasim (IBM, University of Surrey), Adam Nasim (Merck Group, University of Surrey)
Discovering Intrinsic Multi-Compartment Pharmacometric Models Using Physics Informed NeuralNetworks
Imran Nasim (IBM), Joaõ Lucas de Sousa Almeida (IBM)Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems
Maria Luisa Taccari (University of Leeds), He Wang (University College London), Somdatta
Goswami (Johns Hopkins University), Mario De Florio (Brown University), Jonathan Nuttall(Deltares), Xiaohui Chen (University of Leeds), Peter K. Jimack (University of Leeds)
Efficient Groundwater Flow Modeling Using Deep Neural Operators
Zachary M. Miksis (Temple University), Gillian Queisser (Temple University)
A physics-informed neural network for coupled calcium dynamics in a cable neuron
Pavodi Maniamfu(University of Tsukuba), U A Md Ehsan Ali(University of Tsukuba), Keisuke
Kameyama(University of Tsukuba)
PINN with Antisymmetric RNN for Solving Nonlinear Partial Differential Equations
Shujie Liu (University of Waterloo), Justin W.L. Wan (University of Waterloo)
Synthetic Asset Price Paths Generation Using Denoising Diffusion Probabilistic Model

12:30-14:00 Lunch Break (on your own)

14:00-14:50 Invited Talk 1
Shun Sato (The University of Tokyo)
Convergence Rates of Optimization Methods in Continuous and Discrete Time

14:50-15:40 Invited Talk 2
Nicolas Boullé (University of Cambridge)
Elliptic PDE Learning is Provably Data-Efficient

15:40-16:00 Coffee Break

16:00-17:00 Keynote Talk 3
Elena Celledoni (NTNU)
Deep Learning of Diffeomorphisms for Optimal Parametrization of Shapes

March 21st
9:30-10:30 Tutorial 2
Lu Lu (Yale University)
Accurate, Efficient, and Reliable Learning of Deep Neural Operators for Multiphysics and MultiscaleProblems

10:40-11:20 Oral Session 2
Suchuan Dong (Purdue University)
A Variable Projection Method for Computational PDEs with Artificial Neural Networks
Mikhail Tsitsvero (Hokkaido University), Andrey Lyalin (National Institute for Materials Science,Hokkaido University), Mingoo Jin (Hokkaido University)
Towards accurate modeling of dynamics for molecular crystals by scalable variational Gaussian
processes

11:30-12:30 Keynote Talk 4
Masaaki Imaizumi (University of Tokyo)
Statistics for Modern Data Science: Statistical Analysis on Overparameterized Models and In-Context
Learning

12:30- Free Afternoon
Evening: Banquet at Kyoto Research Park

March 22nd
9:30-10:30 Keynote Talk 5
Yuji Nakatsukasa (Oxford University)
Randomized Methods for Matrix and Tensor Computations

10:40-11:20 Oral Session 3
Jun Sasaki (Japan Weather Association), Kenji Utsunomiya (Japan Weather Association), MakiOkada (Japan Weather Association), Koji Yamaguchi (Japan Weather Association)
Hybrid Modeling Approach Using Cloud Dynamics and Deep Learning for Short-term Solar
Forecasting
Alvaro Fernandez (DESY, Universität Hamburg), Nicolás Mendoza (DESY), Armin Iske (UniversitätHamburg), Andrey Yachmenev (DESY, Universität Hamburg), Jochen Küpper (DESY, UniversitätHamburg)
Learning phase-space flows using time-discrete implicit Runge-Kutta PINNs

11:30-12:30 Poster Session 2Yue Shen (The University of Tokyo), Chen Yu (The University of Tokyo)
SPIGAN: A Generative Adversarial Network Supervised by Sparse Identification to Learn
Governing Equations from Scarce Dat
Yuya Note (Kobe University), Takaharu Yaguchi (Kobe University), Toshiaki Omori (KobeUniversity)
Sparse Representation of Koopman Operator
Masataka Konishi (Kwansei Gakuin University), Keiji Miura (Kwansei Gakuin University)
Biologically Plausible Local Synaptic Learning Rules Implement CNNs and Denoising
Autoencoders
Lionel Boillot (TotalEnergies), Frédérik Pivot (TotalEnergies), Félix Klein (TotalEnergies)
Synthetic Label Masks Mapped on Ocean Satellite Background for Oil Seepage Detection
Yusuke Tanaka (NTT)
Learning Hamiltonian dynamics Under Uncertainty via Symplectic Gaussian Processes
Aku Kammonen (KAUST), Lisi Liang (RWTH Aachen), Anamika Pandey (RWTH Aachen), Raúl
Tempone (KAUST, RWTH Aachen)Comparing Spectral Bias and Robustness For Two-Layer Neural Networks: SGD vs Adaptive
Random Fourier Features

12:30-14:00 Lunch Break (on your own)

14:00-15:00 Keynote Talk 6
Molei Tao (Georgia Institute of Technology)
Optimization, Sampling, and Generative Modeling in Non-Euclidean Spaces

15:00-15:50 Invited Talk 3
Nathanael Bosch (University of Tübingen)
Flexible and Efficient Probabilistic Numerical Solvers for Ordinary Differential Equations

15:50-16:10 Coffee Break

16:10-17:10 Keynote Talk 7
Brynjulf Owren (NTNU)
Stability of Numerical Methods on Riemannian Manifolds and Applications to Neural Networks

March 23rd
9:40-10:30 Invited Talk 4
Lena Podina (University of Waterloo)
Universal Physics-Informed Neural Networks and Their Applications

10:40-11:20 Oral Session 4
Identifying Dynamic Regulation with Adversarial Surrogates
Ron Teichner (Technion, Israel Institute of Technology), Naama Brenner (Technion, Israel Instituteof Technology) and Ron Meir (Technion, Israel Institute of Technology)
Neural Networks are Integrable
Yucong Liu (Georgia Institute of Technology)

11:30-12:20 Invited Talk 5
Ayano Kaneda (Waseda University)
Deep Learning Approach to Approximate the Solution for Poisson Matrix

Poster (Online)
Naoya Takeishi (The University of Tokyo, RIKEN)
Toward Bayesian Deep Grey-box Modeling
Aryan Verma (The Ohio State University), Dineshkumar Harursampath (Indian Institute ofScience), Rajnish Mallick (Thapar Institute of Engineering & Technology), Prasant Sahay (IndianInstitute of Science), Krishna Kant Mishra (Indian Institute of Science)
Physics Informed Neural Networks with Application in Computational Structural Mechanics
Takashi Misaka (National Institute of Advanced Industrial Science and Technology (AIST)), Yusuke
Mizuno (National Institute of Advanced Industrial Science and Technology (AIST)), ShogoNakasumi (National Institute of Advanced Industrial Science and TechnoTechnology (AIST)),
Yoshiyuki Furukawa (National Institute of Advanced Industrial Science and Technology (AIST))
Domain-Decomposed Physics-Informed Neural Network Prediction on Cartesian CFD Framework