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Bio: Panos Stinis specializes in scientific computing with application interests in model reduction of complex systems, multiscale modeling, uncertainty quantification, and machine learning. He studied aeronautical engineering at the Technical University of Athens, Greece. He earned his PhD in applied mathematics in 2003, from Columbia University in New York and began his career as a postdoctoral fellow at Lawrence Berkeley National Laboratory and the Stanford Center for Turbulence Research. In 2008, he became a faculty member at the Mathematics Department at the University of Minnesota. He moved to the Pacific Northwest National Laboratory in 2014, where he is currently leading the Computational Mathematics group.

When big neural networks are not enough: physics, multi-fidelity and kernels

Abstract: Modern machine learning has shown remarkable promise in multiple applications. However, brute force use of neural networks, even when they have huge numbers of trainable parameters, can fail to provide highly accurate predictions for problems in the physical sciences. We present a collection of ideas about how enforcing physics, exploiting multi-fidelity knowledge, and the kernel representation of neural networks can lead to a significant increase in efficiency and/or accuracy. Various examples are used to illustrate the ideas.