Deep Neural Operators with Reliable Extrapolation for Multiphysics and Multiscale Problems

Explore the potential of deep neural networks in learning operators of complex systems in this one-hour webinar on deep neural operators. Delve into the universal approximation theorem of operators and its implications for multiphysics and multiscale problems. Learn about the deep operator network (DeepONet) and its extensions, including DeepM&Mnet, POD-DeepONet, (Fourier-)MIONet, and multifidelity DeepONet. Discover applications in diverse fields such as nanoscale heat transport, bubble growth dynamics, high-speed boundary layers, electroconvection, hypersonics, and geological carbon sequestration. Gain insights into addressing the challenge of extrapolation for deep neural operators, including quantifying extrapolation complexity and developing a complete workflow for reliable extrapolation.

DDPS | Deep neural operators with reliable extrapolation for multiphysics & multiscale problems