pwc-archive/papers-with-abstracts huggingface dataset:
Reduced Simulations for High-Energy Physics, a Middle Ground for Data-Driven Physics Research
Authors: Uraz Odyurt, Stephen Nicholas Swatman, Ana-Lucia Varbanescu, Sascha Caron
Date: 2023-08-30 00:00:00
Tasks: Computational Efficiency

Subatomic particle track reconstruction (tracking) is a vital task in High-Energy Physics experiments. Tracking is exceptionally computationally challenging and fielded solutions, relying on traditional algorithms, do not scale linearly. Machine Learning (ML) assisted solutions are a promising answer. We argue that a complexity-reduced problem description and the data representing it, will facilitate the solution exploration workflow. We provide the REDuced VIrtual Detector (REDVID) as a complexity-reduced detector model and particle collision event simulator combo. REDVID is intended as a simulation-in-the-loop, to both generate synthetic data efficiently and to simplify the challenge of ML model design. The fully parametric nature of our tool, with regards to system-level configuration, while in contrast to physics-accurate simulations, allows for the generation of simplified data for research and education, at different levels. Resulting from the reduced complexity, we showcase the computational efficiency of REDVID by providing the computational cost figures for a multitude of simulation benchmarks. As a simulation and a generative tool for ML-assisted solution design, REDVID is highly flexible, reusable and open-source. Reference data sets generated with REDVID are publicly available. Data generated using REDVID has enabled rapid development of multiple novel ML model designs, which is currently ongoing.
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Authors: Kyle Cranmer, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Phiala E. Shanahan
Date: 2023-09-03 00:00:00
Tasks: Audio Generation

Sampling from known probability distributions is a ubiquitous task in computational science, underlying calculations in domains from linguistics to biology and physics. Generative machine-learning (ML) models have emerged as a promising tool in this space, building on the success of this approach in applications such as image, text, and audio generation. Often, however, generative tasks in scientific domains have unique structures and features -- such as complex symmetries and the requirement of exactness guarantees -- that present both challenges and opportunities for ML. This Perspective outlines the advances in ML-based sampling motivated by lattice quantum field theory, in particular for the theory of quantum chromodynamics. Enabling calculations of the structure and interactions of matter from our most fundamental understanding of particle physics, lattice quantum chromodynamics is one of the main consumers of open-science supercomputing worldwide. The design of ML algorithms for this application faces profound challenges, including the necessity of scaling custom ML architectures to the largest supercomputers, but also promises immense benefits, and is spurring a wave of development in ML-based sampling more broadly. In lattice field theory, if this approach can realize its early promise it will be a transformative step towards first-principles physics calculations in particle, nuclear and condensed matter physics that are intractable with traditional approaches.
Forest fire spreading: a nonlinear stochastic model continuous in space and time
Authors: Roberto Beneduci, Giovanni Mascali
Date: 2023-09-01 00:00:00

Forest fire spreading is a complex phenomenon characterized by a stochastic behavior. Nowadays, the enormous quantity of georeferenced data and the availability of powerful techniques for their analysis can provide a very careful picture of forest fires opening the way to more realistic models. We propose a stochastic spreading model continuous in space and time that is able to use such data in their full power. The state of the forest fire is described by the subprobability densities of the green trees and of the trees on fire that can be estimated thanks to data coming from satellites and earth detectors. The fire dynamics is encoded into a density probability kernel which can take into account wind conditions, land slope, spotting phenomena and so on, bringing to a system of integro-differential equations for the probability densities. Existence and uniqueness of the solutions is proved by using Banach's fixed point theorem. The asymptotic behavior of the model is analyzed as well. Stochastic models based on cellular automata can be considered as particular cases of the present model from which they can be derived by space and/or time discretization. Suggesting a particular structure for the kernel, we obtain numerical simulations of the fire spreading under different conditions. For example, in the case of a forest fire evolving towards a river, the simulations show that the probability density of the trees on fire is different from zero beyond the river due to the spotting phenomenon. Firefighters interventions and weather changes can be easily introduced into the model.
Relative representations for cognitive graphs
Authors: Alex B. Kiefer, Christopher L. Buckley
Date: 2023-09-09 00:00:00
Tasks: State Space Models

Although the latent spaces learned by distinct neural networks are not generally directly comparable, recent work in machine learning has shown that it is possible to use the similarities and differences among latent space vectors to derive "relative representations" with comparable representational power to their "absolute" counterparts, and which are nearly identical across models trained on similar data distributions. Apart from their intrinsic interest in revealing the underlying structure of learned latent spaces, relative representations are useful to compare representations across networks as a generic proxy for convergence, and for zero-shot model stitching. In this work we examine an extension of relative representations to discrete state-space models, using Clone-Structured Cognitive Graphs (CSCGs) for 2D spatial localization and navigation as a test case. Our work shows that the probability vectors computed during message passing can be used to define relative representations on CSCGs, enabling effective communication across agents trained using different random initializations and training sequences, and on only partially similar spaces. We introduce a technique for zero-shot model stitching that can be applied post hoc, without the need for using relative representations during training. This exploratory work is intended as a proof-of-concept for the application of relative representations to the study of cognitive maps in neuroscience and AI.
Upper limits on the robustness of Turing models and other multiparametric dynamical systems
Authors: Roozbeh H. Pazuki, Robert G. Endres
Date: 2023-09-09 00:00:00

Traditional linear stability analysis based on matrix diagonalization is a computationally intensive $O(n^3)$ process for $n$-dimensional systems of differential equations, posing substantial limitations for the exploration of Turing systems of pattern formation where an additional wave-number parameter needs to be investigated. In this study, we introduce an efficient $O(n)$ technique that leverages Gershgorin's theorem to determine upper limits on regions of parameter space and the wave number beyond which Turing instabilities cannot occur. This method offers a streamlined avenue for exploring the phase diagrams of other complex multiparametric models, such as those found in systems biology.
Enhancing Phosphorus Uptake in Sugarcane: A Critical Evaluation of Humic Acid and Phosphorus Fertilizers Effectiveness
Authors: Mandana Mirbakhsh, Zahra Zahed
Date: 2023-09-07 00:00:00

Our research conducted in an area characterized by alkaline, lime-abundant soils investigated the potential of utilizing phosphorus fertilizer and humic acid to enhance phosphorus absorption in sugarcane crops. The results indicated that the application of phosphorus fertilizer significantly increased the total and bioavailable phosphorus in the rhizospheric soil, despite observing a decrease in phosphatase enzyme activity. An important observation was the considerable growth of active carbon, a crucial soil health indicator, under the influence of humic acid treatments. The findings also demonstrated an enhancement in phosphorus absorption by sugarcane due to the synergistic application of humic acid and phosphorus fertilizer at both harvest periods. Interestingly, humic acid treatments, when applied through immersion, were found to be more effective than soil applications, implying a greater impact on root absorption processes. The findings underline the potential of integrating humic acid into sugarcane cultivation for better phosphorus absorption. Our study offers valuable insights for improved soil management strategies, and could potentially pave the way towards more sustainable agricultural practices. However, we also recommend further investigation into alternative methods of humic acid application and its usage at different stages of plant growth. Such exploration could provide a comprehensive understanding of the potential benefits and most effective utilization of humic acid in agriculture, especially in regions with similar soil characteristics as West Azarbaijan, Iran
Discrete-to-continuum models of pre-stressed cytoskeletal filament networks
Authors: J. Köry, N. A. Hill, X. Y. Luo, P. S. Stewart
Date: 2023-09-02 00:00:00

We introduce a mathematical model for the mechanical behaviour of the eukaryotic cell cytoskeleton. This discrete model involves a regular array of pre-stressed protein filaments that exhibit resistance to enthalpic stretching, joined at crosslinks to form a network. Assuming that the inter-crosslink distance is much shorter than the lengthscale of the cell, we upscale the discrete force balance to form a continuum system of governing equations and deduce the corresponding macroscopic stress tensor. We use these discrete and continuum models to analyse the imposed displacement of a bead placed in the domain, characterising the cell rheology through the force-displacement curve. We further derive an analytical approximation to the stress and strain fields in the limit of small bead radius, predicting the net force required to generate a given deformation and elucidating the dependency on the microscale properties of the filaments. We apply these models to networks of the intermediate filament vimentin and demonstrate good agreement between predictions of the discrete, continuum and analytical approaches. In particular, our model predicts that the network stiffness increases sublinearly with the filament pre-stress and scales logarithmically with the bead size.
Cooling down and waking up: feedback cooling switches an unconscious neural computer into a conscious quantum computer
Authors: Andrew Bell
Date: 2023-08-21 00:00:00

This paper sets out a theory of how feedback cooling in the brain switches on consciousness. It explains how cooling reduces thermal noise to the point where macroscale quantum phenomena - crucially Bose-Einstein condensation and long-range coherence - can operate at body temperature. It takes the core idea from Stapp that mind and brain interact via some sort of oscillator and then focuses on a likely candidate: neuronal arrays identified by Stapp as cortical minicolumns. Feedback cooling allows amplifiers to act as refrigerators, and when applied to minicolumns it is suggested that the units perform like quantum accelerators, solid-state devices devised to supercharge standard computers. When the accelerator is idle, as in sleep, we have a neural computer operating unconsciously, but when feedback cooling is activated by thalamocortical loops, it produces a Bose-Einstein condensate, quantum computation, and consciousness. The model explains how macroscale quantum phenomena can operate in a warm and noisy brain, how and why consciousness evolved, and gives insight into puzzling unconscious states like sleepwalking. The model is testable, predicting that cold states in the brain are detectable by magnetic resonance thermometry.
Physics-informed machine learning of the correlation functions in bulk fluids
Authors: Wenqian Chen, Peiyuan Gao, Panos Stinis
Date: 2023-09-02 00:00:00
Tasks: Physics-informed machine learning

The Ornstein-Zernike (OZ) equation is the fundamental equation for pair correlation function computations in the modern integral equation theory for liquids. In this work, machine learning models, notably physics-informed neural networks and physics-informed neural operator networks, are explored to solve the OZ equation. The physics-informed machine learning models demonstrate great accuracy and high efficiency in solving the forward and inverse OZ problems of various bulk fluids. The results highlight the significant potential of physics-informed machine learning for applications in thermodynamic state theory.
Band-gap regression with architecture-optimized message-passing neural networks
Authors: Tim Bechtel, Daniel T. Speckhard, Jonathan Godwin, Claudia Draxl
Date: 2023-09-12 00:00:00
Tasks: Band Gap, band gap regression, Neural Architecture Search, regression, Uncertainty Quantification

Graph-based neural networks and, specifically, message-passing neural networks (MPNNs) have shown great potential in predicting physical properties of solids. In this work, we train an MPNN to first classify materials through density functional theory data from the AFLOW database as being metallic or semiconducting/insulating. We then perform a neural-architecture search to explore the model architecture and hyperparameter space of MPNNs to predict the band gaps of the materials identified as non-metals. The parameters in the search include the number of message-passing steps, latent size, and activation-function, among others. The top-performing models from the search are pooled into an ensemble that significantly outperforms existing models from the literature. Uncertainty quantification is evaluated with Monte-Carlo Dropout and ensembling, with the ensemble method proving superior. The domain of applicability of the ensemble model is analyzed with respect to the crystal systems, the inclusion of a Hubbard parameter in the density functional calculations, and the atomic species building up the materials.
Interpretable learning of effective dynamics for multiscale systems
Authors: Emmanuel Menier, Sebastian Kaltenbach, Mouadh Yagoubi, Marc Schoenauer, Petros Koumoutsakos
Date: 2023-09-11 00:00:00

The modeling and simulation of high-dimensional multiscale systems is a critical challenge across all areas of science and engineering. It is broadly believed that even with today's computer advances resolving all spatiotemporal scales described by the governing equations remains a remote target. This realization has prompted intense efforts to develop model order reduction techniques. In recent years, techniques based on deep recurrent neural networks have produced promising results for the modeling and simulation of complex spatiotemporal systems and offer large flexibility in model development as they can incorporate experimental and computational data. However, neural networks lack interpretability, which limits their utility and generalizability across complex systems. Here we propose a novel framework of Interpretable Learning Effective Dynamics (iLED) that offers comparable accuracy to state-of-the-art recurrent neural network-based approaches while providing the added benefit of interpretability. The iLED framework is motivated by Mori-Zwanzig and Koopman operator theory, which justifies the choice of the specific architecture. We demonstrate the effectiveness of the proposed framework in simulations of three benchmark multiscale systems. Our results show that the iLED framework can generate accurate predictions and obtain interpretable dynamics, making it a promising approach for solving high-dimensional multiscale systems.
Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity
Authors: Pascal Pernot
Date: 2023-09-12 00:00:00
Tasks: Uncertainty Quantification

Reliable uncertainty quantification (UQ) in machine learning (ML) regression tasks is becoming the focus of many studies in materials and chemical science. It is now well understood that average calibration is insufficient, and most studies implement additional methods testing the conditional calibration with respect to uncertainty, i.e. consistency. Consistency is assessed mostly by so-called reliability diagrams. There exists however another way beyond average calibration, which is conditional calibration with respect to input features, i.e. adaptivity. In practice, adaptivity is the main concern of the final users of a ML-UQ method, seeking for the reliability of predictions and uncertainties for any point in features space. This article aims to show that consistency and adaptivity are complementary validation targets, and that a good consistency does not imply a good adaptivity. Adapted validation methods are proposed and illustrated on a representative example.
Reaction coordinate flows for model reduction of molecular kinetics
Authors: Hao Wu, Frank Noé
Date: 2023-09-11 00:00:00

In this work, we introduce a flow based machine learning approach, called reaction coordinate (RC) flow, for discovery of low-dimensional kinetic models of molecular systems. The RC flow utilizes a normalizing flow to design the coordinate transformation and a Brownian dynamics model to approximate the kinetics of RC, where all model parameters can be estimated in a data-driven manner. In contrast to existing model reduction methods for molecular kinetics, RC flow offers a trainable and tractable model of reduced kinetics in continuous time and space due to the invertibility of the normalizing flow. Furthermore, the Brownian dynamics-based reduced kinetic model investigated in this work yields a readily discernible representation of metastable states within the phase space of the molecular system. Numerical experiments demonstrate how effectively the proposed method discovers interpretable and accurate low-dimensional representations of given full-state kinetics from simulations.
CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
Authors: Erik Buhmann, Frank Gaede, Gregor Kasieczka, Anatolii Korol, William Korcari, Katja Krüger, Peter McKeown
Date: 2023-09-11 00:00:00
Tasks: CPU

Fast simulation of the energy depositions in high-granular detectors is needed for future collider experiments with ever-increasing luminosities. Generative machine learning (ML) models have been shown to speed up and augment the traditional simulation chain in physics analysis. However, the majority of previous efforts were limited to models relying on fixed, regular detector readout geometries. A major advancement is the recently introduced CaloClouds model, a geometry-independent diffusion model, which generates calorimeter showers as point clouds for the electromagnetic calorimeter of the envisioned International Large Detector (ILD). In this work, we introduce CaloClouds II which features a number of key improvements. This includes continuous time score-based modelling, which allows for a 25-step sampling with comparable fidelity to CaloClouds while yielding a $6\times$ speed-up over Geant4 on a single CPU ($5\times$ over CaloClouds). We further distill the diffusion model into a consistency model allowing for accurate sampling in a single step and resulting in a $46\times$ ($37\times$ over CaloClouds) speed-up. This constitutes the first application of consistency distillation for the generation of calorimeter showers.
Physics-Informed Polynomial Chaos Expansions
Authors: Lukáš Novák, Himanshu Sharma, Michael D. Shields
Date: 2023-09-04 00:00:00
Tasks: Experimental Design, Uncertainty Quantification

Surrogate modeling of costly mathematical models representing physical systems is challenging since it is typically not possible to create a large experimental design. Thus, it is beneficial to constrain the approximation to adhere to the known physics of the model. This paper presents a novel methodology for the construction of physics-informed polynomial chaos expansions (PCE) that combines the conventional experimental design with additional constraints from the physics of the model. Physical constraints investigated in this paper are represented by a set of differential equations and specified boundary conditions. A computationally efficient means for construction of physically constrained PCE is proposed and compared to standard sparse PCE. It is shown that the proposed algorithms lead to superior accuracy of the approximation and does not add significant computational burden. Although the main purpose of the proposed method lies in combining data and physical constraints, we show that physically constrained PCEs can be constructed from differential equations and boundary conditions alone without requiring evaluations of the original model. We further show that the constrained PCEs can be easily applied for uncertainty quantification through analytical post-processing of a reduced PCE filtering out the influence of all deterministic space-time variables. Several deterministic examples of increasing complexity are provided and the proposed method is applied for uncertainty quantification.
Breast MRI radiomics and machine learning radiomics-based predictions of response to neoadjuvant chemotherapy -- how are they affected by variations in tumour delineation?
Authors: Sepideh Hatamikia, Geevarghese George, Florian Schwarzhans, Amirreza Mahbod, Ramona Woitek
Date: 2023-09-03 00:00:00
Tasks: feature selection, Prediction

Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variability. A quantitative assessment of the impact of variations in these delineations on the performance of the radiomics predictors is required to develop robust radiomics based prediction models. In this study, we developed radiomics models for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with two different breast cancer subtypes based on contrast-enhanced magnetic resonance imaging acquired prior to treatment (baseline MRI scans). Different mathematical operations such as erosion, smoothing, dilation, randomization, and ellipse fitting were applied to the original VOIs delineated by experts to simulate variations of segmentation masks. The effects of such VOI modifications on various steps of the radiomics workflow, including feature extraction, feature selection, and prediction performance, were evaluated. Using manual tumor VOIs and radiomics features extracted from baseline MRI scans, an AUC of up to 0.96 and 0.89 was achieved for human epidermal growth receptor 2 positive and triple-negative breast cancer, respectively. For smoothing and erosion, VOIs yielded the highest number of robust features and the best prediction performance, while ellipse fitting and dilation lead to the lowest robustness and prediction performance for both breast cancer subtypes. At most 28% of the selected features were similar to manual VOIs when different VOI delineation data were used. Differences in VOI delineation affects different steps of radiomics analysis, and their quantification is therefore important for development of standardized radiomics research.