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Found 7 papers, 5 papers with code
Half-Inverse Gradients for Physical Deep Learning
Recent works in deep learning have shown that integrating differentiable physics simulators into the training process can greatly improve the quality of results.
Simulating Liquids with Graph Networks
Simulating complex dynamics like fluids with traditional simulators is computationally challenging.
Scale-invariant Learning by Physics Inversion
We find that state-of-the-art training techniques are not well-suited to many problems that involve physical processes.
Physics-based Deep Learning
This digital book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations.
Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers
Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all scientific and engineering disciplines.
Learning to Control PDEs with Differentiable Physics
Predicting outcomes and planning interactions with the physical world are long-standing goals for machine learning.
Learning Time-Aware Assistance Functions for Numerical Fluid Solvers
While our approach is very general and applicable to arbitrary partial differential equation models, we specifically highlight gains in accuracy for fluid flow simulations.
