Brief Description: Methods, systems, and computer-readable media for causal training of physics-informed neural networks (PINNs). The shortcoming of conventional PINNs may be due to the inability of existing PINNs formulations to respect the spatio-temporal causal structure that is inherent to the evolution of physical systems. This is a fundamental limitation and a key source of error that ultimately steers FINN models to converge towards erroneous solutions. Methods can include a re-formulation of PINNs loss functions that can explicitly account for physical causality during model training. This modification alone is enough to introduce significant accuracy improvements, allowing us to tackle problems that have remained elusive to PINNs. Intellectual Property Title: Intellectual Property: Intellectual Property: US 2025/0077875 A1 Docket Number: Docket#: 22-9985
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