4 papers
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…
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…
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…