About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
NeurIPS 2024
Workshop paper
Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks
Abstract
Physics-informed neural networks (PINNs) incorporate physical laws into their training to efficiently solve partial differential equations (PDEs) with minimal data. However, PINNs fail to guarantee adherence to conservation laws, which are also important to consider in modeling physical systems. To address this, we created PINN-Proj, a PINN-based model which uses a novel projection method to enforce to conservation laws. We found that PINN-Proj substantially outperformed PINN in conserving momentum and guaranteed conservation to an accuracy of while performing marginally better in the separate task of state prediction on three PDE datasets.