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We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods.
Statistical machine learning in model predictive control of nonlinear processes
Wu, Z., Rincon, D., Gu, Q., and Christofides, P.D. (2021) · 1912
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A comparison of three methods for selecting values of input variables in the analysis of output from a computer code
McKay, M.D., Beckman, R.J., and Conover, W.J. (1979) · 1979
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On the limited memory BFGS method for large scale optimization
Liu, D.C. and Nocedal, J. (1989) · 1989
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Physics-informed neural nets-based control
Antonelo, E.A., Camponogara, E., Seman, L.O., de Souza, E.R., Jordanou, J.P., and Hubner, J.F. (2021) · 2021
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State–space modeling for control based on physics-informed neural networks
Arnold, F. and King, R. (2021) · 2021
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Karniadakis, G.E., Kevrekidis, I.G., Lu, L., Perdikaris, P., Wang, S., and Yang, L. (2021) · 2021
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An extensible benchmark suite for learning to simulate physical systems
Otness, K., Gjoka, A., Bruna, J., Panozzo, D., Peherstorfer, B., Schneider, T., and Zorin, D. (2021) · 2021
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Sensitivity Analysis and Goal Oriented Error Estimation for Model Predictive Control
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