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Learning locally dominant force balances in active particle systems

D. Sturm, S. Maddu, and I. Sbalzarini. CoRR, (2023)
DOI: 10.48550/ARXIV.2307.14970

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Stability selection enables robust learning of partial differential equations from limited noisy dataS. Maddu, B. Cheeseman, I. Sbalzarini, and C. Müller. (2019)Learning locally dominant force balances in active particle systemsD. Sturm, S. Maddu, and I. Sbalzarini. CoRR, (2023)STENCIL-NET for equation-free forecasting from dataS. Maddu, D. Sturm, B. Cheeseman, C. Müller, and I. Sbalzarini. Scientific Reports, (August 2023)Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problemsS. Perez, S. Maddu, I. Sbalzarini, and P. Poncet. J. Comput. Phys., (2023)Inverse Dirichlet weighting enables reliable training of physics informed neural networksS. Maddu, D. Sturm, C. Müller, and I. Sbalzarini. Machine Learning: Science and Technology, 3 (1): 015026 (February 2022)Learning physically consistent differential equation models from data using group sparsityS. Maddu, B. Cheeseman, C. Müller, and I. Sbalzarini. Phys. Rev. E., 103 (4-1): 042310 (April 2021)Stability selection enables robust learning of differential equations from limited noisy dataS. Maddu, B. Cheeseman, I. Sbalzarini, and C. Müller. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, (June 2022)Parallel Discrete Convolutions on Adaptive Particle Representations of ImagesJ. Jonsson, B. Cheeseman, S. Maddu, K. Gonciarz, and I. Sbalzarini. IEEE Transactions on Image Processing, (2022)

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Learning locally dominant force balances in active particle systems

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Learning locally dominant force balances in active particle systems

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Learning locally dominant force balances in active particle systems