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Adjoint method vs. PINNs: Performance compared for PDE inverse problems

By PulseAugur Editorial · [2 sources] ·

A new paper compares adjoint optimization and physics-informed neural networks (PINNs) for solving inverse problems governed by partial differential equations. The research highlights that the choice of method depends on how the unknown is represented, with grid-based fields favoring adjoint methods and neural representations suiting PINNs. For time-dependent problems, PINNs offer satisfactory reconstructions at a lower cost, and a PINN-warm-started adjoint strategy can achieve adjoint-level accuracy more efficiently. AI

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Read on arXiv cs.LG →

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

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Zhen Zhang, Alessandro Alla, George Em Karniadakis ·

    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 flex…

  2. arXiv cs.LG TIER_1 English(EN) · George Em Karniadakis ·

    Adjoint Method versus Physics-Informed Neural Networks in PDE-Constrained Inverse Problems

    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. Their relative performance remai…

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