Multi fidelity models

Data-driven multi-scale sampling

The pun here is too delicious for me not to use this 1885 cartoon; yes I am aware that marital dynamics are more nuanced amongst adherents of the LDS than the gentleman depicted seems to believe.

At the collision of coarse graining and sampling theory and variational inference, we have multi-fidelity modeling, which is an attempts to harness the efficiency of lower-precision and higher-precision models together. This name is a Machine Learning name; I presume that this concept has been invented many times under other names, which I will add when I learn them. Possibly one of those names is learnable coarse graining


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Wang, Jiang, Stefan Chmiela, Klaus-Robert Müller, Frank Noé, and Cecilia Clementi. 2020. Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach.” The Journal of Chemical Physics 152 (19): 194106.
Wang, Jiang, Simon Olsson, Christoph Wehmeyer, Adrià Pérez, Nicholas E. Charron, Gianni de Fabritiis, Frank Noé, and Cecilia Clementi. 2019. Machine Learning of Coarse-Grained Molecular Dynamics Force Fields.” ACS Central Science 5 (5): 755–67.
Wang, Yuxin, Zheng Xing, and Wei W. Xing. 2023. GAR: Generalized Autoregression for Multi-Fidelity Fusion.” arXiv.
White, Andrew D. 2021. Deep Learning for Molecules and Materials.” Living Journal of Computational Molecular Science 3 (1): 1499–99.
Wurster, Skylar W., Hanqi Guo, Han-Wei Shen, Thomas Peterka, and Jiayi Xu. 2021. Deep Hierarchical Super Resolution for Scientific Data,” May.
Ye, Huilin, Weikang Xian, and Ying Li. 2021. Machine Learning of Coarse-Grained Models for Organic Molecules and Polymers: Progress, Opportunities, and Challenges.” ACS Omega 6 (3): 1758–72.
Zammit-Mangion, Andrew, and Jonathan Rougier. 2019. Multi-Scale Process Modelling and Distributed Computation for Spatial Data,” July.

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