AI RESEARCH
Building Trust in PINNs: Error Estimation through Finite Difference Methods
arXiv CS.AI
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ArXi:2603.15526v1 Announce Type: cross Physics-informed neural networks (PINNs) constitute a flexible deep learning approach for solving partial differential equations (PDEs), which model phenomena ranging from heat conduction to quantum mechanical systems. Despite their flexibility, PINNs offer limited insight into how their predictions deviate from the true solution, hindering trust in their prediction quality.