4 citations · 7 across the 6 of their papers we have counts for
8 papers
Multi-Fidelity Policy Gradients Stabilize Data-Scarce Reinforcement Learning
Xinjie Liu, Ruihan Zhao, Anirban Chaudhuri +3
Policy gradient methods for on-policy reinforcement learning (RL) can become unstable when expensive, scarce target-domain data yield noisy gradient estimates. We address this chal…
Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference
Shane A. McQuarrie, Mengwu Guo, Anirban Chaudhuri
This work develops an active learning framework to intelligently enrich data-driven reduced-order models (ROMs) of parametric dynamical systems, which can serve as the foundation o…
Block-structured Operator Inference for coupled multiphysics model reduction
Benjamin G. Zastrow, Anirban Chaudhuri, Karen E. Willcox +2
This paper presents a block-structured formulation of Operator Inference as a way to learn structured reduced-order models for multiphysics systems. The approach specifies the gove…
Projection-based multifidelity linear regression for data-scarce applications
Vignesh Sella, Julie Pham, Karen Willcox +1
Surrogate modeling for systems with high-dimensional quantities of interest remains challenging, particularly when training data are costly to acquire. This work develops multifide…
TumorTwin: A python framework for patient-specific digital twins in oncology
Michael Kapteyn, Anirban Chaudhuri, Ernesto A. B. F. Lima +5
Background: Advances in the theory and methods of computational oncology have enabled accurate characterization and prediction of tumor growth and treatment response on a patient-s…
Validating the predictions of mathematical models describing tumor growth and treatment response
Guillermo Lorenzo, David A. Hormuth, Chengyue Wu +7
Despite advances in methods to interrogate tumor biology, the observational and population-based approach of classical cancer research and clinical oncology does not enable anticip…
Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems
Shane A. McQuarrie, Anirban Chaudhuri, Karen E. Willcox +1
This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use…
Multifidelity linear regression for scientific machine learning from scarce data
Elizabeth Qian, Dayoung Kang, Vignesh Sella +1
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…