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Found 5 papers, 4 papers with code
Multifidelity linear regression for scientific machine learning from scarce data
Machine learning (ML) methods, which fit to data the parameters of a given parameterized model class, have garnered significant interest as potential methods for learning surrogate models for complex engineering systems for which traditional simulation is expensive.
Energy-Preserving Reduced Operator Inference for Efficient Design and Control
Many-query computations, in which a computational model for an engineering system must be evaluated many times, are crucial in design and control.
Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference
We introduce Gramian definitions relevant to the inference setting and propose a balanced truncation approach based on these inference Gramians that yield a reduced dynamical system that can be used to cheaply approximate the posterior mean and covariance.
Reduced operator inference for nonlinear partial differential equations
First, ideas from projection-based model reduction are used to explicitly parametrize the learned model by low-dimensional polynomial operators which reflect the known form of the governing PDE.
Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
The lifting map is applied to data obtained by evaluating a model for the original nonlinear system.
BIG-bench Machine Learning
Physics-informed machine learning
