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Differentiable Physics: A Position Piece
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Differentiable physics provides a new approach for modeling and understanding the physical systems by pairing the new technology of differentiable programming with classical numerical methods for physical simulation. We survey the rapidly growing literature of differentiable physics techniques and highlight methods for parameter estimation, learning representations, solving differential equations, and developing what we call scientific foundation models using data and inductive priors. We argue that differentiable physics offers a new paradigm for modeling physical phenomena by combining classical analytic solutions with numerical methodology using the bridge of differentiable programming.
Forward citations
Cited by 3 Pith papers
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Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks
Differentiable physics recovers accurate wall shear stress from concentration observations across measurement scenarios where PINNs fail.
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What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning
NeuralPDEs trained on finite-difference simulation data inherit the discretization's Taylor-series truncation error, so generalization depends on matching numerical schemes between the solver and the model.
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Open source Differentiable ODE Solving Infrastructure
DeepChem gets differentiable ODE solvers adapted from ξ-torch, validated on four standard benchmarks, but the reported accuracy and open-source code are not yet verifiable.
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