p7physics.comp-ph

physics.comp-ph

68 papers

Molecular geometry prediction using a deep generative graph neural network

Elman Mansimov, Omar Mahmood, Seokho Kang, Kyunghyun Cho

1904.00314

Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines

Li Huang, Lei Wang

1610.02746

Forecasting Sequential Data using Consistent Koopman Autoencoders

Omri Azencot, N. Benjamin Erichson, Vanessa Lin, Michael W. Mahoney

2003.02236

Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows

Suraj Pawar, Shady E. Ahmed, Omer San, Adil Rasheed, Ionel M. Navon

2005.11296

Lagrangian Neural Networks

Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia, David Spergel, Shirley Ho

2003.04630

Hamiltonian Graph Networks with ODE Integrators

Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, Peter Battaglia

1909.12790

Robust and efficient configurational molecular sampling via Langevin Dynamics

Benedict Leimkuhler, Charles Matthews

1304.3269

Community detection in graphs

Santo Fortunato

0906.0612

Benchmark graphs for testing community detection algorithms

Andrea Lancichinetti, Santo Fortunato, Filippo Radicchi

0805.4770

Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders

Romit Maulik, Bethany Lusch, Prasanna Balaprakash

2002.00470

Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems

Maziar Raissi, Paris Perdikaris, George Em Karniadakis

1801.01236

Discovering Symbolic Models from Deep Learning with Inductive Biases

Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho

2006.11287

Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty

1909.12077

Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B. Tenenbaum, Antonio Torralba

1810.01566

On Learning Hamiltonian Systems from Data

Tom Bertalan, Felix Dietrich, Igor Mezić, Ioannis G. Kevrekidis

1907.12715

Using nonequilibrium fluctuation theorems to understand and correct errors in equilibrium and nonequilibrium discrete Langevin dynamics simulations

David A. Sivak, John D. Chodera, Gavin E. Crooks

1107.2967

Rational Construction of Stochastic Numerical Methods for Molecular Sampling

Benedict Leimkuhler, Charles Matthews

1203.5428

Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data

Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris

1901.06314

Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Prediction

N. Benjamin Erichson, Michael Muehlebach, Michael W. Mahoney

1905.10866

Accurate sampling using Langevin dynamics

Giovanni Bussi, Michele Parrinello

0803.4083

Time step rescaling recovers continuous-time dynamical properties for discrete-time Langevin integration of nonequilibrium systems

David A. Sivak, John D. Chodera, Gavin E. Crooks

1301.3800

Discretization errors in molecular dynamics simulations with deterministic and stochastic thermostats

Ruslan L. Davidchack

1412.7067

The Adjoint Petrov-Galerkin Method for Non-Linear Model Reduction

Eric J. Parish, Christopher Wentland, Karthik Duraisamy

1810.03455

Learning to Simulate Complex Physics with Graph Networks

Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter W. Battaglia

2002.09405

DiffTaichi: Differentiable Programming for Physical Simulation

Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, Frédo Durand

1910.00935

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

John A. Keith, Valentin Vassilev-Galindo, Bingqing Cheng, Stefan Chmiela, Michael Gastegger, Klaus-Robert Müller, Alexandre Tkatchenko

2102.06321

Adversarial Uncertainty Quantification in Physics-Informed Neural Networks

Yibo Yang, Paris Perdikaris

1811.04026

Targeted free energy estimation via learned mappings

Peter Wirnsberger, Andrew J. Ballard, George Papamakarios, Stuart Abercrombie, Sébastien Racanière, Alexander Pritzel, Danilo Jimenez Rezende, Charles Blundell

2002.04913

Large Language Models

Michael R. Douglas

2307.05782

Adaptive Thermostats for Noisy Gradient Systems

Benedict Leimkuhler, Xiaocheng Shang

1505.06889

Community detection algorithms: a comparative analysis

Andrea Lancichinetti, Santo Fortunato

0908.1062

Uniformly Accurate Machine Learning Based Hydrodynamic Models for Kinetic Equations

Jiequn Han, Chao Ma, Zheng Ma, Weinan E

1907.03937

Communities in Networks

Mason A. Porter, Jukka-Pekka Onnela, Peter J. Mucha

0902.3788

Deep Learning with Coherent Nanophotonic Circuits

Yichen Shen, Nicholas C. Harris, Scott Skirlo, Mihika Prabhu, Tom Baehr-Jones, Michael Hochberg, Xin Sun, Shijie Zhao, Hugo Larochelle, Dirk Englund, Marin Soljacic

1610.02365

A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer

Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker, Jörg Behler

2009.06484

Equivariant Flows: sampling configurations for multi-body systems with symmetric energies

Jonas Köhler, Leon Klein, Frank Noé

1910.00753

Corrections to Einstein's relation for Brownian motion in a tilted periodic potential

J. C. Latorre, G. A. Pavliotis, P. R. Kramer

1208.2150

Machine Learning in QM/MM Molecular Dynamics Simulations of Condensed-Phase Systems

Lennard Böselt, Moritz Thürlemann, Sereina Riniker

2010.11610

PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network

Zichao Long, Yiping Lu, Bin Dong

1812.04426

Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks

David Pfau, James S. Spencer, Alexander G. de G. Matthews, W. M. C. Foulkes

1909.02487

Multiresolution community detection for megascale networks by information-based replica correlations

Peter Ronhovde, Zohar Nussinov

0812.1072

Model Reduction with Memory and the Machine Learning of Dynamical Systems

Chao Ma, Jianchun Wang, Weinan E

1808.04258

Global Optimization of Copper Clusters at the ZnO(10-10) Surface Using a DFT-based Neural Network Potential and Genetic Algorithms

Martín Leandro Paleico, Jörg Behler

2007.06459

Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning

Kevin T. Carlberg, Antony Jameson, Mykel J. Kochenderfer, Jeremy Morton, Liqian Peng, Freddie D. Witherden

1812.01177

Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics

Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E

1707.09571

Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

Dongkun Zhang, Lu Lu, Ling Guo, George Em Karniadakis

1809.08327

Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

Ameya D. Jagtap, George Em Karniadakis

1906.01170

Hard-sphere melting and crystallization with event-chain Monte Carlo

Masaharu Isobe, Werner Krauth

1505.07896

Fermionic neural-network states for ab-initio electronic structure

Kenny Choo, Antonio Mezzacapo, Giuseppe Carleo

1909.12852

Coercing Machine Learning to Output Physically Accurate Results

Zhenglin Geng, Dan Johnson, Ronald Fedkiw

1910.09671

CosmoFlow: Using Deep Learning to Learn the Universe at Scale

Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Daina Moise, Simon J. Pennycook, Kristyn Maschoff, Jason Sewall, Nalini Kumar, Shirley Ho, Mike Ringenburg, Prabhat, Victor Lee

1808.04728

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties

Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé

2008.08461

Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks

Alexandre M. Tartakovsky, Carlos Ortiz Marrero, Paris Perdikaris, Guzel D. Tartakovsky, David Barajas-Solano

1808.03398

Reinforced dynamics for enhanced sampling in large atomic and molecular systems

Linfeng Zhang, Han Wang, Weinan E

1712.03461

Detecting the overlapping and hierarchical community structure of complex networks

Andrea Lancichinetti, Santo Fortunato, Janos Kertesz

0802.1218

GPU Accelerated Discrete Element Method (DEM) Molecular Dynamics for Conservative, Faceted Particle Simulations

Matthew Spellings, Ryan L. Marson, Joshua A. Anderson, Sharon C. Glotzer

1607.02427

RUMD: A general purpose molecular dynamics package optimized to utilize GPU hardware down to a few thousand particles

Nicholas P. Bailey, Trond S. Ingebrigtsen, Jesper Schmidt Hansen, Arno A. Veldhorst, Lasse Bøhling, Claire A. Lemarchand, Andreas E. Olsen, Andreas K. Bacher, Lorenzo Costigliola, Ulf R. Pedersen, Heine Larsen, Jeppe C. Dyre, Thomas B. Schrøder

1506.05094

Lifting -- A nonreversible Markov chain Monte Carlo Algorithm

Marija Vucelja

1412.8762

Machine Learning the Physical Non-Local Exchange-Correlation Functional of Density-Functional Theory

Jonathan Schmidt, Carlos L. Benavides-Riveros, Miguel A. L. Marques

1908.06198

EikoNet: Solving the Eikonal equation with Deep Neural Networks

Jonathan D. Smith, Kamyar Azizzadenesheli, Zachary E. Ross

2004.00361

Designing nanostructures for interfacial phonon transport via Bayesian optimization

Shenghong Ju, Takuma Shiga, Lei Feng, Zhufeng Hou, Koji Tsuda, Junichiro Shiomi

1609.04972

Machine learning for molecular simulation

Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, Cecilia Clementi

1911.02792

MCMC using Hamiltonian dynamics

Radford M. Neal

1206.1901

Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities