4.8k citations · 20.2k across the 183 of their papers we have counts for
199 papers
Physics-Informed Neural Network Surrogates with Polynomial Chaos-Based Uncertainty Propagation for Stochastic Model Predictive Control
Srimanta Santra, Romi Patel, Saikat Mukherjee +2
Stochastic partial differential equations (PDEs) govern critical engineering and geophysical systems but are challenging to use for real-time control under parametric uncertainty.…
Data-driven linear analysis of dynamical systems via nonlinearity-subtracted dynamic mode decomposition
Benjamin Herrmann, Katherine Cao, Steven L. Brunton +1
The Dynamic Mode Decomposition (DMD) has been consolidated as a basic tool for data-driven analysis of dynamical systems, allowing simultaneous identification of coherent structure…
Active Learning for Calibrating Entangling Gates via Surrogate-Based Optimization
Caleb Walton, Patricia García-Caspueñas, Filippo Zacchei +3
The fidelity of a quantum gate is sensitive to small deviations in the physical control parameters. Unfortunately, it is generally difficult to exactly model the implemented Hamilt…
Data-driven linear analysis of turbulent flows
Benjamin Herrmann, Katherine Cao, Carlos A. Gonzalez +2
Mean-flow-based linear analyses of turbulent flows, such as resolvent analysis, provide valuable insight about flow structures and their dynamics that has been widely leveraged to…
Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements
Filippo Zacchei, Ana Larrañaga, Attilio Frangi +2
Data from simulations and experiments are rarely noise-free and often exhibit heterogeneous levels of fidelity. Measurement uncertainty may vary across repeated observations, sensi…
Uncovering Extreme Event Mechanisms for Prediction and Control with Sensitivity-Balanced Projections
Nicholas Zolman, Sajeda Mokbel, Samuel E. Otto +1
Extreme events -- such as earthquakes and coronal mass ejections -- are common in many chaotic dynamical systems, yet are difficult to characterize and predict due to the subtle in…