| Inverse Dirichlet weighting enables reliable training of physics informed neural networks S Maddu, D Sturm, CL Müller, IF Sbalzarini Machine Learning: Science and Technology 3 (1), 015026, 2022 | 86 | 2022 |
| Direct numerical simulation of coupled fluid flow and heat transfer for single particles and particle packings by a LBM-approach H Kruggel-Emden, B Kravets, MK Suryanarayana, R Jasevicius Powder Technology 294, 236-251, 2016 | 77 | 2016 |
| Stability selection enables robust learning of differential equations from limited noisy data S Maddu, BL Cheeseman, IF Sbalzarini, CL Müller Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2022 | 50* | 2022 |
| Learning physically consistent differential equation models from data using group sparsity S Maddu, BL Cheeseman, CL Müller, IF Sbalzarini Physical Review E 103 (4), 042310, 2021 | 25 | 2021 |
| Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems S Perez, S Maddu, IF Sbalzarini, P Poncet Journal of Computational Physics 491, 112342, 2023 | 23 | 2023 |
| Lattice Boltzmann method for thin-liquid-film hydrodynamics S Zitz, A Scagliarini, SM Kondaiah, AA Darhuber, J Harting Phys. Rev. E, 0 | 21* | |
| STENCIL-NET for equation-free forecasting from data S Maddu, D Sturm, BL Cheeseman, CL Müller, IF Sbalzarini Scientific reports 13 (1), 12787, 2023 | 16* | 2023 |
| Parallel discrete convolutions on adaptive particle representations of images J Jonsson, BL Cheeseman, S Maddu, K Gonciarz, IF Sbalzarini IEEE Transactions on Image Processing 31, 4197-4212, 2022 | 4 | 2022 |
| Learning deterministic hydrodynamic equations from stochastic active particle dynamics S Maddu, Q Vagne, IF Sbalzarini | 3* | |
| Learning fast, accurate, and stable closures of a kinetic theory of an active fluid S Maddu, S Weady, MJ Shelley Journal of Computational Physics 504, 112869, 2024 | 2 | 2024 |
| Stochastic force inference via density estimation V Chardès, S Maddu, MJ Shelley NeurIPS 2023 AI for Science Workshop, 2023 | 1 | 2023 |
| Learning locally dominant force balances in active particle systems D Sturm, S Maddu, IF Sbalzarini Proceedings A 480 (2304), 20230532, 2024 | | 2024 |
| Inferring biological processes with intrinsic noise from cross-sectional data S Maddu, V Chardès, M Shelley arXiv preprint arXiv:2410.07501, 2024 | | 2024 |
| The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning R Ohana, M McCabe, LT Meyer, R Morel, FJ Agocs, M Beneitez, M Berger, ... The Thirty-eight Conference on Neural Information Processing Systems …, 2024 | | 2024 |
| Learning accurate closures of a kinetic theory of an active fluid S Maddu, S Weady, M Shelley Bulletin of the American Physical Society, 2023 | | 2023 |
| Data-driven modeling and simulation of spatiotemporal processes with a view toward applications in biology S Maddu Kondaiah Dissertation, Dresden, Technische Universität Dresden, 2021, 2021 | | 2021 |
| Data-driven modeling and simulation of spatiotemporal processes with a view toward applications in biology S Maddu University of Dresden, Germany, 2021 | | 2021 |
| Robust Uncertainty Quantification and Automatic Weighting of Multi-Objective and Multi-Scale Bayesian Physics-Informed Neural Networks. S Perez, S Maddu, P Poncet, IF Sbalzarini AGU24, 0 | | |
| ENCODING KNOWLEDGE WITH GROUP SPARSITY FOR MODEL LEARNING FROM LIMITED AND NOISY BIOLOGICAL DATA S Maddu, BL Cheeseman, CL Müller, IF Sbalzarini | | |
| Learning computable models from data S Maddu, D Sturm, BL Cheeseman, CL Müller, IF Sbalzarini 14th World Congress on Computational Mechanics (WCCM), ECCOMAS Congress 2020, 0 | | |
Ivo F. SbalzariniTU Dresden & Max Planck Institute of Molecular Cell Biology and GeneticsVerified email at mpi-cbg.de
