arXiv Machine Learning
Benchmarking Optimizers to Solve Inverse Problems with Differentiable Physics Simulators
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jun 11
Adjoint Method versus Physics-Informed Neural Networks in PDE-Constrained Inverse Problems
arXiv:2606. 12337v1 Announce Type: cross Abstract: Inverse problems governed by partial differential equations (PDEs) are central to computational mechanics and are commonly solved by adjoint-based optimization, while physics-informed neural networks (PINNs) have emerged as a flexible alternative.
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.
arXiv AI
Jul 1
Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems
arXiv:2508. 09156v3 Announce Type: replace-cross Abstract: We present a framework for fine-tuning flow-matching generative models to enforce physical constraints and solve inverse problems in scientific systems.
arXiv Machine Learning
Jul 9
Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
arXiv:2607. 07682v1 Announce Type: new Abstract: The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces.
arXiv Machine Learning
Aug 21
Guided Diffusion by Optimized Loss Functions on Relaxed Parameters for Inverse Material Design
arXiv:2602. 15648v2 Announce Type: replace Abstract: Inverse design problems are common in engineering and materials science.
Hugging Face Trending Papers
Jul 8
Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
The inverse design of physical systems governed by partial differential equations is computationally demanding due to the high dimensionality and non-convexity of design spaces. Generative models for inverse design often lack robustness and transferability, whereas evolutionary strategies are robust but struggle in high-dimensional spaces.