Physics-constrained machine learning for scientific computing - Amazon Science

Amazon researchers draw inspiration from finite-volume methods and adapt neural operators to enforce conservation laws and boundary conditions in deep-learning models of physical systems.

Machine learning Physics-constrained machine learning for scientific computing Amazon researchers draw inspiration from finite-volume methods and adapt neural operators to enforce conservation laws and boundary conditions in deep-learning models of physical systems. By Danielle Maddix Robinson , Shima Alizadeh , Gaurav Gupta May 16, 2023 7 min read Share Share Copy link Email X LinkedIn Facebook Line Reddit QZone Sina Weibo WeChat WhatsApp 分享到微信 x Conference ICLR 2023 ICML 2023 Related publications Guiding continuous operator learning through physics-based boundary constraints Learning physica

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