Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospects

TL;DR

This paper reviews recent AI-based methods for solving inverse PDE problems, highlighting their applications, challenges, and future directions across scientific and industrial domains.

Contribution

It provides the first comprehensive, systematic overview of AI approaches to inverse PDE problems, categorizing methods and discussing future research prospects.

Findings

AI methods are transforming inverse PDE problem-solving.

Applications span medical imaging, geophysics, and aerodynamics.

Open challenges include data limitations and uncertainty quantification.

Abstract

Solving inverse partial differential equation (PDE) problems is a fundamental topic in scientific research due to its broad significance across a wide range of real-world applications. Inverse PDE problems arise across medical imaging, geophysics, materials science, and aerodynamics, where the goal is to infer hidden causes, design structures, or control physical states. In this paper, we provide a comprehensive review of recent advances in solving inverse PDE problems using artificial intelligence (AI). We first introduce the basic formulation, key challenges, and traditional numerical foundations of inverse PDE problems, and then organize it into three major categories: inverse problems, inverse design, and control problems. For each category, we further present a methodological paradigms, and review representative state-of-the-art approaches from recent years. We then summarize representative applications across scientific and industrial domains, including mechanical systems, aerodynamic problems, thermal systems, full-waveform inversion, system identification, and medical imaging. Finally, we discuss open challenges and future prospects, such as physics-informed architectures, limited real-world data, uncertainty quantification, and inverse foundation models. This survey aims to provide the first unified and systematic perspective on AI for inverse PDE problems, demonstrating how modern learning-based methods are reshaping inverse problems, inverse design, and control problems in PDE-governed systems.