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New arXiv papers benchmark optimizers and debias PINNs for inverse problems

By PulseAugur Editorial · [2 sources] ·

Two new arXiv papers explore solving inverse problems using differentiable physics simulators and physics-informed neural networks (PINNs). The first paper benchmarks various optimizers across 12 differentiable physics simulators to identify effective strategies for parameter identification, inverse design, and optimal control. The second paper proposes a debiased estimation procedure for PDE-constrained inverse problems using PINNs, achieving a $\sqrt{n}$-consistent and asymptotically normal estimator by correcting for bias in the neural network's nuisance function. AI

IMPACT These papers advance methods for solving complex scientific and engineering problems by improving the efficiency and statistical validity of AI-driven simulation and parameter estimation.

RANK_REASON Two academic papers published on arXiv detailing new methods for solving inverse problems.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New arXiv papers benchmark optimizers and debias PINNs for inverse problems

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Xiang Chen, Huanhuan Xia ·

    Benchmarking Optimizers to Solve Inverse Problems with Differentiable Physics Simulators

    arXiv:2609.13819v1 Announce Type: new Abstract: Solving inverse problems with differentiable physics simulators holds the potential to revolutionize scientific discovery and engineering design, as it enjoys both the strict physical correctness from rigorous numerical physics simu…

  2. arXiv stat.ML TIER_1 English(EN) · Yves Atchade, Debarghya Mukherjee ·

    PDE-constrained inverse problems at the $\sqrt{n}$ rate via debiased physics-informed neural networks

    arXiv:2609.12301v1 Announce Type: cross Abstract: We study the problem of estimating unknown parameters in PDE-constrained inverse problems from noisy observations, where the PDE solution is approximated using Physics-Informed Neural Networks (PINNs). While PINNs have demonstrate…

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