Hugging Face Trending Papers

Differentiate the Solver, Not the Equation: Reverse-Sweep Adjoints for Block Implicit Simulation

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Differentiable simulation is a key component in learning, control, and inverse problems, where gradients through nonlinear implicit solvers are required. Existing approaches either rely on unrolled automatic differentiation, whose memory grows with solver depth, or on equation-level implicit differentiation, which assembles global Jacobians and solves large sparse adjoint systems, discarding the locality of the forward solver -- and differentiating the converged equation rather than the finite computation that actually ran.

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arXiv Machine Learning
Sep 3

GRADSOLVE: fast exact gradients for ODE ensembles on GPUs

GRADSOLVE is an open‑source JAX library that provides fast, exact reverse‑mode gradients for low‑dimensional ordinary differential equation (ODE) ensembles on NVIDIA GPUs. It records the accepted steps of an adaptive solver and differentiates a fixed‑step replay, yielding the exact discrete adjoint at a lower computational cost than traditional checkpointed methods. Benchmarks show that GRADSOLVE’s forward kernel is 2.8× faster than DiffEqGPU.jl, and its gradient computation is 5.6–14.1× faster than Diffrax’s checkpointed adjoint while maintaining matched forward‑state accuracy across multiple GPU generations.

By Alessio Spurio Mancini
arXiv Machine Learning
Jun 29

Mosaic: A Benchmark Suite for Differentiable Physics Solvers

arXiv:2606. 27895v1 Announce Type: cross Abstract: Differentiable partial differential equation (PDE) solvers underpin solver-in-the-loop ML training, gradient-based optimal control, and inverse problems, yet the practical cost of obtaining correct, usable gradients from a given solver on a given problem is largely undocumented.

By Andrin Rehmann, Heiko Zimmermann, Dion H\"afner