Efficient Calibration for Black-Box Physics-Based Models

Learn efficient calibration techniques for black-box physics-based models in this 55-minute conference talk presented by Franca Hoffmann from the California Institute of Technology at IPAM's Mathematics and Machine Learning for Earth System Simulation Workshop. Explore advanced mathematical approaches and machine learning methodologies specifically designed to calibrate complex physics-based models where internal mechanisms are not directly accessible or observable. Discover how these calibration techniques can be applied to earth system simulations and other scientific computing applications where accurate parameter estimation is crucial for model reliability and predictive performance. Gain insights into the intersection of mathematics, machine learning, and computational physics as applied to environmental and earth science modeling challenges.