Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets

作者：Paris Perdikaris, Sifan Wang, Hanwen Wang · 年份：2021 · DOI：10.26226/morressier.612f6736bc98103724100850 · 被引用次数：48 · 研究领域：Model Reduction and Neural Networks、Lattice Boltzmann Simulation Studies、Heat Transfer and Optimization

Machine Learning (ML) and Digital Twins (DT) are at the heart of today’s different industries, ranging from advanced manufacturing to biomedical systems to resilient ecosystems, civil infrastructures, smart cities, and healthcare. They have become indispensable for solving complex problems in science, engineering, and technology development. The purpose of the MMLDT-CSET 2021 conference is to facilitate the transition of ML and DT from fundamental research to mainstream fields and technologies through advanced data science, mechanistic methods, and computational technologies. This 3-day conference features technical tracks of emerging ML-DT fields and applications, special public lectures, short courses, and demonstrations. The conference will be held in a hybrid format, featuring both on-site and virtual sessions.