A Physics-informed Deep Learning Approach for Minimum Effort Stochastic Control of Colloidal Self-Assembly-Reference-Cited by-同舟云学术

A Physics-informed Deep Learning Approach for Minimum Effort Stochastic Control of Colloidal Self-Assembly

Published:2023-05-31 Issue: Volume: Page:
ISSN:
Container-title:2023 American Control Conference (ACC)
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Author:

Nodozi Iman¹,O’Leary Jared²,Mesbah Ali²,Halder Abhishek³

Affiliation:

1. University of California,Department of Electrical and Computer Engineering,Santa Cruz,CA,USA,95064

2. University of California,Department of Chemical and Biomolecular Engineering,Berkeley,CA,USA,94720

3. University of California,Department of Applied Mathematics,Santa Cruz,CA,USA,95064

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10155646/10155787/10156176.pdf?arnumber=10156176

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3. Stochastic Control Liaisons: Richard Sinkhorn Meets Gaspard Monge on a Schrödinger Bridge

4. On a quasi-linear parabolic equation occurring in aerodynamics

5. Schrödinger meets Kuramoto via Feynman- Kac: Minimum effort distribution steering for noisy nonuniform Kuramoto oscillators;nodozi;2022 IEEE Conference on Decision and Control (CDC) arXiv 2202 09734,2022

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3. Neural Schrödinger Bridge With Sinkhorn Losses: Application to Data-Driven Minimum Effort Control of Colloidal Self-Assembly;IEEE Transactions on Control Systems Technology;2023