Physics-based modeling and simulation has become indispensable across many applications in science and engineering, ranging from autonomous-vehicle control to designing new materials. However, achieving high predictive fidelity necessitates modeling fine spatiotemporal resolution, which can lead to extreme-scale computational models whose simulations consume months on thousands of computing cores. This constitutes a formidable computational barrier: The cost of truly high-fidelity simulations renders them impractical for important time-critical applications (e.g., rapid design, control, real-time simulation) in engineering and science.
In this talk, speaker Kevin Carlberg will present several advances in the field of nonlinear model reduction that leverage machine-learning techniques ranging from convolutional autoencoders to LSTM networks to overcome this barrier. In particular, these methods produce low-dimensional counterparts to high-fidelity models called reduced-order models (ROMs) that exhibit accuracy, low cost, physical-property preservation, guaranteed generalization performance, and error quantification.