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Timothy Praditia

2 accepted papers

2022

Composing Partial Differential Equations with Physics-Aware Neural Networks

ICML 2022spotlight

We introduce a compositional physics-aware FInite volume Neural Network (FINN) for learning spatiotemporal advection-diffusion processes. FINN implements a new way of combining the learning abilities of artificial neural networks with physical and structural knowledge from numerical simulation by mo…

2022

PDEBench: An Extensive Benchmark for Scientific Machine Learning

NeurIPS 2022accept

Machine learning-based modeling of physical systems has experienced increased interest in recent years. Despite some impressive progress, there is still a lack of benchmarks for Scientific ML that are easy to use but still challenging and repre- sentative of a wide range of problems. We introduce PD…