no code implementations • 21 Feb 2024 • Shirin Panahi, Ling-Wei Kong, Mohammadamin Moradi, Zheng-Meng Zhai, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
Recent research on the Atlantic Meridional Overturning Circulation (AMOC) raised concern about its potential collapse through a tipping point due to the climate-change caused increase in the freshwater input into the North Atlantic.
1 code implementation • 15 Nov 2023 • Zheng-Meng Zhai, Mohammadamin Moradi, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
In particular, with training data from a subset of the dynamical variables of the system for a small number of known parameter values, the framework is able to accurately predict the parameter variations in time.
1 code implementation • Nature Communications 2023 • Zheng-Meng Zhai, Mohammadamin Moradi, Ling-Wei Kong, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
We develop a model-free, machine-learning framework to control a two-arm robotic manipulator using only partially observed states, where the controller is realized by reservoir computing.
no code implementations • 5 Oct 2022 • Ling-Wei Kong, Yang Weng, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
We articulate the design imperatives for machine-learning based digital twins for nonlinear dynamical systems subject to external driving, which can be used to monitor the ``health'' of the target system and anticipate its future collapse.
no code implementations • 15 Mar 2021 • Chen-Di Han, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
In particular, we develop a deep learning algorithm according to some physics motivated loss function based on the Heisenberg equation, which "forces" the neural network to follow the quantum evolution of the spin variables.
no code implementations • 25 Feb 2021 • Chen-Di Han, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
The rapid growth of research in exploiting machine learning to predict chaotic systems has revived a recent interest in Hamiltonian Neural Networks (HNNs) with physical constraints defined by the Hamilton's equations of motion, which represent a major class of physics-enhanced neural networks.