4 papers
Data-Driven Modeling and Correction of Vehicle Dynamics
Nguyen Ly, Caroline Tatsuoka, Jai Nagaraj +4
We develop a data-driven framework for learning and correcting non-autonomous vehicle dynamics. Physics-based vehicle models are often simplified for tractability and therefore exh…
Exact Conditional Score-Guided Generative Modeling for Amortized Inference in Uncertainty Quantification
Zezhong Zhang, Caroline Tatsuoka, Dongbin Xiu +1
We propose an efficient framework for amortized conditional inference by leveraging exact conditional score-guided diffusion models to train a non-reversible neural network as a co…
Bifidelity Parameter Estimation Using Conditional Diffusion Models
Caroline Tatsuoka, Minglei Yang, Dongbin Xiu +1
We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sample the target conditional distr…
Deep learning for model correction of dynamical systems with data scarcity
Caroline Tatsuoka, Dongbin Xiu
We present a deep learning framework for correcting existing dynamical system models utilizing only a scarce high-fidelity data set. In many practical situations, one has a low-fid…