The following collection of materials targets "Physics-Based Deep Learning" (PBDL), i.e., the field of methods with combinations of physical modeling and deep learning (DL) techniques. Here, DL will typically refer to methods based on artificial neural networks. The general direction of PBDL represents a very active and quickly growing field of research.
If you're interested in a comprehensive overview, please check our digital PBDL book: https://www.physicsbaseddeeplearning.org/ (or as PDF: https://arxiv.org/pdf/2109.05237.pdf)
Within this area, we can distinguish a variety of different physics-based approaches, from targeting designs, constraints, combined methods, and optimizations to applications. More specifically, all approaches either target forward simulations (predicting state or temporal evolution) or inverse problems (e.g., obtaining a parametrization for a physical system from observations). Apart from forward or inverse, the type of integration between learning and physics gives a means for categorizing different methods:
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Data-driven: the data is produced by a physical system (real or simulated), but no further interaction exists.
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Loss-terms: the physical dynamics (or parts thereof) are encoded in the loss function, typically in the form of differentiable operations. The learning process can repeatedly evaluate the loss, and usually receives gradients from a PDE-based formulation.
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Interleaved: the full physical simulation is interleaved and combined with an output from a deep neural network; this requires a fully differentiable simulator and represents the tightest coupling between the physical system and the learning process. Interleaved approaches are especially important for temporal evolutions, where they can yield an estimate of future behavior of the dynamics.
Thus, methods can be roughly categorized in terms of forward versus inverse solve, and how tightly the physical model is integrated into the optimization loop that trains the deep neural network. Here, especially approaches that leverage differentiable physics allow for very tight integration of deep learning and numerical simulations.
This repository collects links to works on deep learning algorithms for physics problems, with a particular emphasis on fluid flow, i.e., Navier-Stokes related problems. It primarily collects links to the work of the I15 lab at TUM, as well as miscellaneous works from other groups. This is by no means a complete list, so let us know if you come across additional papers in this area. We intentionally also focus on works from the deep learning field, not machine learning in general.
Flow Matching for Posterior Inference with Simulator Feedback , PDF: https://arxiv.org/pdf/2410.22573
APEBench: A Benchmark for Autoregressive Neural Emulators of PDEs , PDF: http://arxiv.org/pdf/2411.00180
Deep learning-based predictive modelling of transonic flow over an aerofoil , PDF: https://arxiv.org/pdf/2403.17131
ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks , Project: https://tum-pbs.github.io/ConFIG/
The Unreasonable Effectiveness of Solving Inverse Problems with Neural Networks , PDF: http://arxiv.org/pdf/2408.08119
Phiflow: Differentiable Simulations for PyTorch, TensorFlow and Jax , PDF: https://openreview.net/pdf/36503358a4f388f00d587a0257c13ba2a4656098.pdf
How Temporal Unrolling Supports Neural Physics Simulators , PDF: https://arxiv.org/pdf/2402.12971
Stabilizing Backpropagation Through Time to Learn Complex Physics , PDF: https://openreview.net/forum?id=bozbTTWcaw
Symmetric Basis Convolutions for Learning Lagrangian Fluid Mechanics , PDF: https://openreview.net/forum?id=HKgRwNhI9R
Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models , Project: https://github.com/tum-pbs/Diffusion-based-Flow-Prediction
Turbulent Flow Simulation using Autoregressive Conditional Diffusion Models , Project: https://github.com/tum-pbs/autoreg-pde-diffusion
Physics-Preserving AI-Accelerated Simulations of Plasma Turbulence , PDF: https://arxiv.org/pdf/2309.16400>
Unsteady Cylinder Wakes from Arbitrary Bodies with Differentiable Physics-Assisted Neural Network , Project: https://github.com/tum-pbs/DiffPhys-CylinderWakeFlow
Score Matching via Differentiable Physics , Project: https://github.com/tum-pbs/SMDP
Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics , Project: https://github.com/tum-pbs/DMCF
Learned Turbulence Modelling with Differentiable Fluid Solvers , Project: https://github.com/tum-pbs/differentiable-piso
Half-Inverse Gradients for Physical Deep Learning , Project: https://github.com/tum-pbs/half-inverse-gradients
Reviving Autoencoder Pretraining (Previously: Data-driven Regularization via Racecar Training for Generalizing Neural Networks), Project: https://github.com/tum-pbs/racecar
Realistic galaxy images and improved robustness in machine learning tasks from generative modelling , PDF: https://arxiv.org/pdf/2203.11956
Hybrid Neural Network PDE Solvers for Reacting Flows , Project: https://github.com/tum-pbs/Hybrid-Solver-for-Reactive-Flows
Scale-invariant Learning by Physics Inversion (formerly "Physical Gradients") , Project: https://github.com/tum-pbs/SIP
High-accuracy transonic RANS Flow Predictions with Deep Neural Networks , Project: https://github.com/tum-pbs/coord-trans-encoding
Learning Meaningful Controls for Fluids , Project: https://rachelcmy.github.io/den2vel/
Global Transport for Fluid Reconstruction with Learned Self-Supervision , Project: https://ge.in.tum.de/publications/2021-franz-globtrans
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers , Project: https://github.com/tum-pbs/Solver-in-the-Loop
Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates , PDF: https://arxiv.org/pdf/2009.14339
Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution , PDF: https://arxiv.org/pdf/2008.08626
Latent Space Subdivision: Stable and Controllable Time Predictions for Fluid Flow , Project: https://ge.in.tum.de/publications/2020-lssubdiv-wiewel
WeatherBench: A benchmark dataset for data-driven weather forecasting , Project: https://github.com/pangeo-data/WeatherBench
Learning Similarity Metrics for Numerical Simulations (LSiM) , Project: https://ge.in.tum.de/publications/2020-lsim-kohl
Learning to Control PDEs with Differentiable Physics , Project: https://ge.in.tum.de/publications/2020-iclr-holl
Lagrangian Fluid Simulation with Continuous Convolutions , PDF: https://openreview.net/forum?id=B1lDoJSYDH
Tranquil-Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds , Project: https://ge.in.tum.de/publications/2020-iclr-prantl/
ScalarFlow: A Large-Scale Volumetric Data Set of Real-world Scalar Transport Flows for Computer Animation and Machine Learning , Project: https://ge.in.tum.de/publications/2019-tog-eckert/
tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow , Project: https://ge.in.tum.de/publications/tempogan/
Deep Fluids: A Generative Network for Parameterized Fluid Simulations , Project: http://www.byungsoo.me/project/deep-fluids/
Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow , Project: https://ge.in.tum.de/publications/latent-space-physics/
A Multi-Pass GAN for Fluid Flow Super-Resolution , PDF: https://ge.in.tum.de/publications/2019-multi-pass-gan/
A Study of Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations , Project: https://github.com/thunil/Deep-Flow-Prediction
Data-Driven Synthesis of Smoke Flows with CNN-based Feature Descriptors , Project: http://ge.in.tum.de/publications/2017-sig-chu/
Liquid Splash Modeling with Neural Networks , Project: https://ge.in.tum.de/publications/2018-mlflip-um/
Generating Liquid Simulations with Deformation-aware Neural Networks , Project: https://ge.in.tum.de/publications/2017-prantl-defonn/
DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction , PDF: https://arxiv.org/pdf/2402.02425
Inferring Hybrid Neural Fluid Fields from Videos , PDF: https://openreview.net/pdf?id=kRdaTkaBwC
LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite , Project: https://github.com/tumaer/lagrangebench
CFDBench: A Comprehensive Benchmark for Machine Learning Methods in Fluid Dynamics , PDF: https://arxiv.org/pdf/2310.05963.pdf
Physics-guided training of GAN to improve accuracy in airfoil design synthesis , PDF: https://arxiv.org/pdf/2308.10038
A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty , PDF: https://arxiv.org/pdf/2307.02432
Differentiable Turbulence , PDF: https://arxiv.org/pdf/2307.03683
Super-resolving sparse observations in partial differential equations: A physics-constrained convolutional neural network approach , PDF: https://arxiv.org/pdf/2306.10990
Reduced-order modeling of fluid flows with transformers , PDF: https://pubs.aip.org/aip/pof/article/35/5/057126/2891586
Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs , PDF: https://arxiv.org/pdf/2305.09199
Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement , PDF: https://arxiv.org/pdf/2304.12130
Inferring Fluid Dynamics via Inverse Rendering , PDF: https://arxiv.org/pdf/2304.04446
Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics , WWW: https://aip.scitation.org/doi/10.1063/5.0097679
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions , PDF: https://arxiv.org/pdf/2212.07564
Exploring Physical Latent Spaces for Deep Learning , PDF: https://arxiv.org/pdf/2211.11298
Modelling spatiotemporal turbulent dynamics with the convolutional autoencoder echo state network , PDF: https://arxiv.org/pdf/2211.11379
Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics , PDF: https://arxiv.org/pdf/2211.00307
NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields , Project: https://github.com/syguan96/NeuroFluid
Lagrangian Large Eddy Simulations via Physics Informed Machine Learning , PDF: https://arxiv.org/pdf/2207.04012
Learning to Estimate and Refine Fluid Motion with Physical Dynamics , PDF: https://arxiv.org/pdf/2206.10480.pdf
Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulations , PDF: https://arxiv.org/pdf/2206.11038
Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions , PDF: https://arxiv.org/pdf/2205.11912
Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data , Project: https://rachelcmy.github.io/pinf_smoke/
Leveraging Stochastic Predictions of Bayesian Neural Networks for Fluid Simulations , PDF: https://arxiv.org/pdf/2205.01222
NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields , PDF: https://arxiv.org/pdf/2203.01762.pdf
Deep neural networks to correct sub-precision errors in CFD , PDF: https://arxiv.org/pdf/2202.04233
Deep learning fluid flow reconstruction around arbitrary two-dimensional objects from sparse sensors using conformal mappings , PDF: https://arxiv.org/pdf/2202.03798.pdf
Predicting Physics in Mesh-reduced Space with Temporal Attention , PDF: https://arxiv.org/pdf/2201.09113.pdf
Inferring Turbulent Parameters via Machine Learning , PDF: https://arxiv.org/pdf/2201.00732
Learned Coarse Models for Efficient Turbulence Simulation , PDF: https://arxiv.org/pdf/2112.15275.pdf
Deep Learning for Stability Analysis of a Freely Vibrating Sphere at Moderate Reynolds Number , PDF: https://arxiv.org/pdf/2112.09858.pdf
Predicting High-Resolution Turbulence Details in Space and Time , PDF: http://geometry.caltech.edu/pubs/BWDL21.pdf
Assessments of model-form uncertainty using Gaussian stochastic weight averaging for fluid-flow regression , PDF: https://arxiv.org/pdf/2109.08248.pdf
Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks , PDF: https://arxiv.org/pdf/2109.03327
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows , PDF: https://arxiv.org/pdf/2109.01514.pdf
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets , PDF: https://arxiv.org/pdf/2108.07667.pdf
Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid Simulations , PDF: https://arxiv.org/pdf/2107.04556.pdf
Learning Incompressible Fluid Dynamics from Scratch - Towards Fast, Differentiable Fluid Models that Generalize , PDF: https://cg.cs.uni-bonn.de/aigaion2root/attachments/Paper.pdf
Scientific multi-agent reinforcement learning for wall-models of turbulent flows , PDF: https://arxiv.org/pdf/2106.11144.pdf
Simulating Continuum Mechanics with Multi-Scale Graph Neural Networks , PDF: https://arxiv.org/pdf/2106.04900.pdf
Embedded training of neural-network sub-grid-scale turbulence models , PDF: https://arxiv.org/pdf/2105.01030.pdf
Optimal control of point-to-point navigation in turbulent time dependent flows using Reinforcement Learning , PDF: https://arxiv.org/pdf/2103.00329.pdf
Machine learning accelerated computational fluid dynamics , PDF: https://arxiv.org/pdf/2102.01010.pdf
Neural Particle Image Velocimetry , PDF: https://arxiv.org/pdf/2101.11950.pdf
A turbulent eddy-viscosity surrogate modeling framework for Reynolds-Averaged Navier-Stokes simulations , Project+Code: https://www.sciencedirect.com/science/article/abs/pii/S0045793020303479
Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels , PDF: https://arxiv.org/pdf/2011.02364.pdf
A Point-Cloud Deep Learning Framework for Prediction of Fluid Flow Fields on Irregular Geometries , PDF: https://arxiv.org/pdf/2010.09469
Learning Mesh-Based Simulations with Graph Networks , PDF: https://arxiv.org/pdf/2010.03409
Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations , PDF: https://arxiv.org/pdf/2010.00072
Learning to swim in potential flow , PDF: https://arxiv.org/pdf/2009.14280
A neural network multigrid solver for the Navier-Stokes equations , PDF: https://arxiv.org/pdf/2008.11520.pdf
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization , PDF: https://arxiv.org/pdf/2008.06731
Learned discretizations for passive scalar advection in a 2-D turbulent flow , PDF: https://arxiv.org/pdf/2004.05477
PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parameterized Steady-State PDEs on Irregular Domain , PDF: https://arxiv.org/pdf/2004.13145
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction , PDF: https://proceedings.icml.cc/static/paper_files/icml/2020/6414-Paper.pdf
CFDNet: A deep learning-based accelerator for fluid simulations , PDF: https://arxiv.org/pdf/2005.04485
The neural particle method--an updated Lagrangian physics informed neural network for computational fluid dynamics , PDF: https://arxiv.org/pdf/2003.10208
Controlling Rayleigh-Benard convection via Reinforcement Learning , PDF: https://arxiv.org/pdf/2003.14358
Embedding Hard Physical Constraints in Neural Network Coarse-Graining of 3D Turbulence , PDF: https://arxiv.org/pdf/2002.00021
Learning to Simulate Complex Physics with Graph Networks , PDF: https://arxiv.org/pdf/2002.09405
DPM: A deep learning PDE augmentation method (with application to large-eddy simulation) , PDF: https://arxiv.org/pdf/1911.09145
Towards Physics-informed Deep Learning for Turbulent Flow Prediction , PDF: https://arxiv.org/pdf/1911.08655
Dynamic Upsampling of Smoke through Dictionary-based Learning , PDF: https://arxiv.org/pdf/1910.09166
Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers , PDF: https://arxiv.org/pdf/1908.10515
DeepFlow: History Matching in the Space of Deep Generative Models , PDF: https://arxiv.org/pdf/1905.05749
Deep learning observables in computational fluid dynamics , PDF: https://arxiv.org/pdf/1903.03040
Compressed convolutional LSTM: An efficient deep learning framework to model high fidelity 3D turbulence , PDF: https://arxiv.org/pdf/1903.00033
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data , PDF: https://arxiv.org/pdf/1901.06314.pdf
Deep neural networks for data-driven LES closure models , PDF: https://www.sciencedirect.com/science/article/pii/S0021999119306151
Computing interface curvature from volume fractions: A machine learning approach , PDF: https://www.sciencedirect.com/science/article/abs/pii/S0045793019302282
Machine learning the kinematics of spherical particles in fluid flows , PDF: https://sandlab.mit.edu/wp-content/uploads/18_JFM.pdf
Deep Neural Networks for Data-Driven Turbulence Models , PDF: https://export.arxiv.org/pdf/1806.04482
Deep Dynamical Modeling and Control of Unsteady Fluid Flows , PDF: http://papers.nips.cc/paper/8138-deep-dynamical-modeling-and-control-of-unsteady-fluid-flows
Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids , Project+Code: http://dpi.csail.mit.edu
Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient , PDF: https://arxiv.org/pdf/1712.10082
Prediction of laminar vortex shedding over a cylinder using deep learning , PDF: https://arxiv.org/pdf/1712.07854
Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks , PDF: https://arxiv.org/pdf/1705.09036
Reasoning About Liquids via Closed-Loop Simulation , PDF: https://arxiv.org/pdf/1703.01656
Prediction model of velocity field around circular cylinder over various Reynolds numbers by fusion convolutional neural networks based on pressure on the cylinder , PDF: https://doi.org/10.1063/1.5024595
Accelerating Eulerian Fluid Simulation With Convolutional Networks , Project+Code: https://cims.nyu.edu/~schlacht/CNNFluids.htm
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance , PDF: https://www.labxing.com/files/lab_publications/2259-1524535041-QiPuSd6O.pdf
Zero-shot forecasting of chaotic systems , PDF: https://arxiv.org/pdf/2409.15771
Generative Learning for Forecasting the Dynamics of Complex Systems , PDF: https://arxiv.org/pdf/2402.17157
Micro-Macro Consistency in Multiscale Modeling: Score-Based Model Assisted Sampling of Fast/Slow Dynamical Systems , PDF: https://arxiv.org/pdf/2312.05715
Machine Learning for Partial Differential Equations , PDF: https://arxiv.org/pdf/2303.17078.pdf
Learning to Accelerate Partial Differential Equations via Latent Global Evolution , Project: http://snap.stanford.edu/le_pde/
Implicit Neural Spatial Representations for Time-dependent PDEs , PDF: https://proceedings.mlr.press/v202/chen23af/chen23af.pdf
Noise-aware physics-informed machine learning for robust PDE discovery , PDF: https://iopscience.iop.org/article/10.1088/2632-2153/acb1f0/pdf
Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal Forecasts , PDF: https://arxiv.org/pdf/2302.11101.pdf
Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems , PDF: https://arxiv.org/pdf/2301.10391
Continuous PDE dynamics forecasting with implicit neural representations , PDF: https://arxiv.org/pdf/2209.14855
Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion , PDF: https://arxiv.org/pdf/2208.03322
Discovering nonlinear pde from scarce data with physics encoded learning , PDF: https://arxiv.org/pdf/2201.12354.pdf
CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations , PDF: https://arxiv.org/pdf/2206.02607.pdf
Learning to Solve PDE-constrained Inverse Problems with Graph Networks , Project: https://cyanzhao42.github.io/LearnInverseProblem
CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method , PDF: https://arxiv.org/pdf/2110.15832
Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling , PDF: https://arxiv.org/pdf/2106.07218v1.pdf
Learning Functional Priors and Posteriors from Data and Physics , PDF: https://arxiv.org/pdf/2106.05863.pdf
Accelerating Neural ODEs Using Model Order Reduction , PDF: https://arxiv.org/pdf/2105.14070
Adversarial Multi-task Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations , PDF: https://arxiv.org/pdf/2104.14320
gradSim: Differentiable simulation for system identification and visuomotor control , Project: https://gradsim.github.io
Physics-aware, probabilistic model order reduction with guaranteed stability , PDF: https://arxiv.org/pdf/2101.05834
Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks , PDF: https://arxiv.org/pdf/2012.03133.pdf
Aphynity: Augmenting physical models with deep networks for complex dynamics forecasting , PDF: https://arxiv.org/pdf/2010.04456.pdf
Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers , PDF: https://arxiv.org/pdf/2008.09768
Learning Compositional Koopman Operators for Model-Based Control , Project: http://koopman.csail.mit.edu
Universal Differential Equations for Scientific Machine Learning , PDF: https://arxiv.org/pdf/2001.04385.pdf
Understanding and mitigating gradient pathologies in physics-informed neural networks , PDF: https://arxiv.org/pdf/2001.04536
Variational Physics-Informed Neural Networks For Solving Partial Differential Equations , PDF: https://arxiv.org/pdf/1912.00873
Poisson CNN: Convolutional Neural Networks for the Solution of the Poisson Equation with Varying Meshes and Dirichlet Boundary Conditions , PDF: https://arxiv.org/pdf/1910.08613
IDENT: Identifying Differential Equations with Numerical Time evolution , PDF: https://arxiv.org/pdf/1904.03538
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network , PDF: https://arxiv.org/pdf/1812.04426
Data-driven discretization: a method for systematic coarse graining of partial differential equations , PDF: https://arxiv.org/pdf/1808.04930
Solving high-dimensional partial differential equations using deep learning , PDF: https://www.pnas.org/content/115/34/8505.full.pdf
Neural Ordinary Differential Equations , PDF: https://arxiv.org/pdf/1806.07366
Deep Learning the Physics of Transport Phenomena , PDF: https://arxiv.org/pdf/1709.02432
DGM: A deep learning algorithm for solving partial differential equations , PDF: https://arxiv.org/pdf/1708.07469
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations , PDF: https://arxiv.org/pdf/1708.00588
Data-assisted reduced-order modeling of extreme events in complex dynamical systems , Project+Code: https://github.com/zhong1wan/data-assisted
PDE-Net: Learning PDEs from Data , Project+Code: https://github.com/ZichaoLong/PDE-Net
Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems , PDF: https://arxiv.org/pdf/1708.06850
Neural-network-based approximations for solving partial differential equations , DOI: https://doi.org/10.1002/cnm.1640100303
Accelerating Legacy Numerical Solvers by Non-intrusive Gradient-based Meta-solving , PDF: https://arxiv.org/pdf/2405.02952
Physics-embedded Fourier Neural Network for Partial Differential Equations ,
PDF: https://arxiv.org/pdf/2407.11158
Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations , Project: https://jhagnberger.github.io/vectorized-conditional-neural-field/
UM2N: Towards Universal Mesh Movement Networks , PDF: https://arxiv.org/pdf/2407.00382
Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media ,
PDF: https://arxiv.org/pdf/2405.19019
Hybrid Modeling Design Patterns , PDF: https://arxiv.org/pdf/2401.00033
ClimODE: Climate Forecasting With Physics-informed Neural ODEs , PDF: https://openreview.net/pdf?id=xuY33XhEGR
Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs , PDF: https://arxiv.org/pdf/2403.12553
Investigation of the generalization capability of a generative adversarial network for large eddy simulation of turbulent premixed reacting flows , PDF: https://www.sciencedirect.com/science/article/pii/S1540748922002851
Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks , PDF: https://arxiv.org/pdf/2311.13949
Neural General Circulation Models , PDF: https://arxiv.org/pdf/2311.07222
A neural-preconditioned poisson solver for mixed dirichlet and neumann boundary conditions , PDF: https://arxiv.org/pdf/2310.00177.pdf
Neural stream functions , PDF: https://arxiv.org/pdf/2307.08142.pdf
Stabilized Neural Differential Equations for Learning Constrained Dynamics , PDF: https://arxiv.org/pdf/2306.09739
Combining Slow and Fast: Complementary Filtering for Dynamics Learning , PDF: https://arxiv.org/pdf/2302.13754.pdf
PDEBench: An Extensive Benchmark for Scientific Machine Learning , Project: https://github.com/pdebench/PDEBench
Efficient Learning of Mesh-Based Physical Simulation with Bi-Stride Multi-Scale Graph Neural Network , PDF: https://arxiv.org/pdf/2210.02573.pdf
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls , PDF: https://arxiv.org/pdf/2212.07959
Breaking Bad: A Dataset for Geometric Fracture and Reassembly , Project: https://arxiv.org/pdf/2210.11463
Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data , PDF: https://arxiv.org/pdf/2208.00971
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs , PDF: https://arxiv.org/pdf/2206.11990
Symplectically Integrated Symbolic Regression of Hamiltonian Dynamical Systems , PDF: https://arxiv.org/pdf/2209.01521.pdf
Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics , PDF: https://arxiv.org/pdf/2205.02835.pdf
Message Passing Neural PDE Solvers , PDF: https://arxiv.org/pdf/2202.03376
A Survey on Machine Learning Approaches for Modelling Intuitive Physics , PDF: https://arxiv.org/pdf/2202.06481
Fine-grained differentiable physics: a yarn-level model for fabrics , PDF: https://arxiv.org/pdf/2202.00504
Accurately Solving Rod Dynamics with Graph Learning , PDF: http://computationalsciences.org/publications/shao-2021-physical-systems-graph-learning/shao-2021-physical-systems-graph-learning.pdf
Constraint-based graph network simulator , PDF: https://arxiv.org/pdf/2112.09161
Differentiable Simulation of Soft Multi-body Systems , PDF: https://arxiv.org/pdf/2205.01758
Learning Material Parameters and Hydrodynamics of Soft Robotic Fish via Differentiable Simulation , PDF: https://arxiv.org/pdf/2109.14855
Model Reduction for the Material Point Method via Learning the Deformation map and its Spatial-temporal Gradients , Project: https://peterchencyc.com/projects/rom4mpm/
PhysGNN: A Physics–Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image–Guided Neurosurgery , PDF: https://arxiv.org/pdf/2109.04352.pdf
Deep learning for surrogate modelling of 2D mantle convection , PDF: https://arxiv.org/pdf/2108.10105
An Extensible Benchmark Suite for Learning to Simulate Physical Systems , PDF: https://arxiv.org/pdf/2108.07799
Turbulent field fluctuations in gyrokinetic and fluid plasmas , PDF: https://arxiv.org/pdf/2107.09744.pdf
Robust Value Iteration for Continuous Control Tasks , PDF: https://arxiv.org/pdf/2105.12189
Fast and Feature-Complete Differentiable Physics for Articulated Rigid Bodies with Contact , PDF: https://arxiv.org/pdf/2103.16021
High-order Differentiable Autoencoder for Nonlinear Model Reduction , PDF: https://arxiv.org/pdf/2102.11026.pdf
Modeling of the nonlinear flame response of a Bunsen-type flame via multi-layer perceptron , Paper: https://www.sciencedirect.com/science/article/pii/S1540748920305666
Deluca – A Differentiable Control Library: Environments, Methods, and Benchmarking , PDF: https://montrealrobotics.ca/diffcvgp/assets/papers/1.pdf
Deep Energy-based Modeling of Discrete-Time Physics , PDF: https://proceedings.neurips.cc/paper/2020/file/98b418276d571e623651fc1d471c7811-Paper.pdf
NeuralSim: Augmenting Differentiable Simulators with Neural Networks , PDF: https://arxiv.org/pdf/2011.04217.pdf
Fourier Neural Operator for Parametric Partial Differential Equations , PDF: https://arxiv.org/pdf/2010.08895.pdf
Learning Composable Energy Surrogates for PDE Order Reduction , PDF: https://arxiv.org/pdf/2005.06549.pdf
Transformers for Modeling Physical Systems , PDF: https://arxiv.org/pdf/2010.03957
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics , PDF: https://proceedings.icml.cc/static/paper_files/icml/2020/1323-Paper.pdf
Scalable Differentiable Physics for Learning and Control , PDF: https://proceedings.icml.cc/static/paper_files/icml/2020/15-Paper.pdf
Cloth in the Wind: A Case Study of Physical Measurement through Simulation , PDF: https://arxiv.org/pdf/2003.05065
Learning to Slide Unknown Objects with Differentiable Physics Simulations , PDF: https://arxiv.org/pdf/2005.05456
Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics , Project: https://github.com/USC-Melady/ICLR2020-PADGN
Differentiable Molecular Simulations for Control and Learning , PDF: https://arxiv.org/pdf/2003.00868
Incorporating Symmetry into Deep Dynamics Models for Improved Generalization , PDF: https://arxiv.org/pdf/2002.03061
Learning to Measure the Static Friction Coefficient in Cloth Contact , PDF: https://hal.inria.fr/hal-02511646
Learning to Simulate Complex Physics with Graph Networks , PDF: https://arxiv.org/pdf/2002.09405
Hamiltonian Neural Networks , PDF: http://papers.nips.cc/paper/9672-hamiltonian-neural-networks.pdf
Interactive Differentiable Simulation , PDF: https://arxiv.org/pdf/1905.10706
DiffTaichi: Differentiable Programming for Physical Simulation , PDF: https://arxiv.org/pdf/1910.00935
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data , PDF: https://arxiv.org/pdf/1901.06314
COPHY: Counterfactual Learning of Physical Dynamics , Project: https://github.com/fabienbaradel/cophy
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations , Project: http://physadept.csail.mit.edu
End-to-End Differentiable Physics for Learning and Control , Project+Code: https://github.com/locuslab/lcp-physics
Stochastic seismic waveform inversion using generative adversarial networks as a geological prior , PDF: https://arxiv.org/pdf/1806.03720
Learning to Optimize Multigrid PDE Solvers , PDF: http://proceedings.mlr.press/v97/greenfeld19a/greenfeld19a.pdf
Latent-space Dynamics for Reduced Deformable Simulation , Project+Code: http://www.dgp.toronto.edu/projects/latent-space-dynamics/
Learning-Based Animation of Clothing for Virtual Try-On , PDF: http://www.gmrv.es/Publications/2019/SOC19/
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning , PDF: https://openreview.net/pdf?id=BklHpjCqKm
Flexible Neural Representation for Physics Prediction , Project+Code: https://neuroailab.github.io/physics/
Robust Reference Frame Extraction from Unsteady 2D Vector Fields with Convolutional Neural Networks , PDF: https://arxiv.org/pdf/1903.10255
Physics-as-Inverse-Graphics: Joint Unsupervised Learning of Objects and Physics from Video , PDF: https://arxiv.org/pdf/1905.11169
Unsupervised Intuitive Physics from Past Experiences , PDF: https://arxiv.org/pdf/1905.10793
Reasoning About Physical Interactions with Object-Oriented Prediction and Planning , PDF: https://arxiv.org/pdf/1812.10972
Neural Material: Learning Elastic Constitutive Material and Damping Models from Sparse Data , PDF: https://arxiv.org/pdf/1808.04931
Discovering physical concepts with neural networks , PDF: https://arxiv.org/pdf/1807.10300
Fluid directed rigid body control using deep reinforcement learning , Project: http://gamma.cs.unc.edu/DRL_FluidRigid/
DeepMimic, Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills , PDF: https://arxiv.org/pdf/1804.02717
Unsupervised Intuitive Physics from Visual Observations , PDF: https://arxiv.org/pdf/1805.05086
Graph networks as learnable physics engines for inference and control , PDF: https://arxiv.org/pdf/1806.01242
DeepWarp: DNN-based Nonlinear Deformation , PDF: https://arxiv.org/pdf/1803.09109
A proposal on machine learning via dynamical systems , Journal: https://link.springer.com/article/10.1007/s40304-017-0103-z
Interaction Networks for Learning about Objects, Relations and Physics , PDF: https://arxiv.org/pdf/1612.00222
Physics-Guided Deep Learning for Dynamical Systems: A Survey , PDF: https://arxiv.org/pdf/2107.01272
Integrating Physics-Based Modeling with Machine Learning: A Survey , PDF: https://arxiv.org/pdf/2003.04919
Integrating Machine Learning with Physics-Based Modeling , PDF: https://arxiv.org/pdf/2006.02619
A review on Deep Reinforcement Learning for Fluid Mechanics , PDF: https://arxiv.org/pdf/1908.04127
Machine Learning for Fluid Mechanics , PDF: https://arxiv.org/pdf/1905.11075
PhiFlow: https://github.com/tum-pbs/phiflow
Diff-Taichi: https://github.com/yuanming-hu/difftaichi
Jax-Md: https://github.com/google/jax-md
TensorFlow-Foam: https://github.com/argonne-lcf/TensorFlowFoam
Julia-Sciml: https://sciml.ai
GradSim: https://gradsim.github.io
Jax-Cfd: https://github.com/google/jax-cfd
Jax-Fluids: https://github.com/tumaer/JAXFLUIDS
Physics-based deep learning is a very dynamic field. Please let us know if we've overlooked papers that you think should be included by sending a mail to i15ge at cs.tum.de, and feel free to check out our homepage at https://ge.in.tum.de/.