Publications
Journals
- Maia, M.A.; Rocha, I.B.C.M.; Kerfriden, P.; Van der Meer, F.P. (2023). Physically recurrent neural networks for path-dependent heterogeneous materials: Embedding constitutive models in a data-driven surrogate. Computer Methods in Applied Mechanics and Engineering. [https://www.sciencedirect.com/science/article/pii/S0045782523000579]
- Rocha, I.B.C.M; Kerfriden, P.; Van der Meer, F.P. (2023). Machine learning of evolving physics-based material models for multiscale solid mechanics. Mechanics of Materials. [https://www.sciencedirect.com/science/article/pii/S0167663623001539]
- Dobson, P.; Bierkens, J. (2023). Infinite dimensional Piecewise Deterministic Markov Processes. Stochastic Processes and their Applications. [https://www.sciencedirect.com/science/article/pii/S0304414923001771]
- Kekkonen, H.; Lassas, M.; Saaksman, E.; Siltanen, S. (2023). Random tree Besov priors – Towards fractal imaging. Inverse Problems and Imaging. [https://www.aimsciences.org/article/doi/10.3934/ipi.2022059?viewType=HTML]
- Barroso, E.S.; Ribeiro, L.G.; Maia, M.A; Rocha, I.B.C.M.; Parente Jr, E.; Melo, A.M.C. (2022). BIOS: an object-oriented framework for Surrogate-Based Optimization using bio-inspired algorithms. Structural and Multidisciplinary Optimization. [https://link.springer.com/article/10.1007/s00158-022-03302-0]
- Rocha, I.B.C.M, Kerfriden, P. and , Van der Meer, F.P. (2021). On-the-fly construction of surrogate constitutive models for concurrent multiscale mechanical analysis through probabilistic machine learning. Journal of Computational Physics. [https://www.sciencedirect.com/science/article/pii/S2590055220300354]
- Rocha, I.B.C.M, Kerfriden, P. and , Van der Meer, F.P. (2020). Micromechanics-based surrogate models for the response of composites: A critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks. European Journal of Mechanics - A/Solids. [https://www.sciencedirect.com/science/article/pii/S0997753820300565]
- Rocha, I.B.C.M, Van der Meer, F.P. Mororo, L.A.T. and Sluys, L.I. (2020). Accelerating crack growth simulations through adaptive model order reduction. International Journal for Numerical Methods in Engineering. [https://ui.adsabs.harvard.edu/abs/2020IJNME.121.2147R/abstract]
- Rocha, I.B.C.M, Van der Meer, F.P. and Sluys, L.I. (2020). An adaptive domain-based POD/ECM hyper-reduced modeling framework without offline training. Computer Methods in Applied Mechanics and Engineering. [https://www.sciencedirect.com/science/article/pii/S0045782519305353]
- Rocha, I.B.C.M, Van der Meer, F.P. and Sluys, L.I. (2019). Efficient micromechanical analysis of fiber-reinforced composites subjected to cyclic loading through time homogenization and reduced-order modeling. Computer Methods in Applied Mechanics and Engineering. [https://www.sciencedirect.com/science/article/abs/pii/S004578251830570X]
Conferences
- Rocha, I.B.C.M, Kerfriden, P. and , Van der Meer, F.P. (2023). Machine Learning of Evolving Material Models for Multiscale Analysis of Fiber-reinforced Composites. 9th ECCOMAS Thematic Conference on the Mechanical Response of Composites. [https://composites2023.cimne.com/event/contribution/e82b5869-a661-11ed-b019-000c29ddfc0c]
- Maia, M.A.; Rocha, I.B.C.M.; Van der Meer, F.P. (2023). Physically recurrent neural networks for microscale analysis of rate-dependent off-axis unidirectional laminates. 9th ECCOMAS Thematic Conference on the Mechanical Response of Composites. [https://composites2023.cimne.com/event/contribution/e65b5d71-a640-11ed-b019-000c29ddfc0c]
- Van der Meer, F.P. (2023). Multiscale modeling of failure in composites. 9th ECCOMAS Thematic Conference on the Mechanical Response of Composites. [https://composites2023.cimne.com/event/contribution/a691f5b2-1cc2-11ee-9a1c-000c29ddfc0c]
- Rocha, I.B.C.M; Van der Meer, F.P.; Sluys, L.J. (2023). Bias-variance tradeoff in accelerating multiscale solid mechanics with model order reduction and machine learning. Seventh International Conference on Computational Modeling of Fracture and Failure of Materials and Structures (CFRAC 2023). [http://mech.fsv.cvut.cz/cfrac/abstracts/MSA-354.pdf]
- Maia, M.A.; Kovacs, N.; Rocha, I.B.C.M.; Van der Meer, F.P. (2023). Physically recurrent neural networks for homogenization of path-dependent heterogeneous materials. Seventh International Conference on Computational Modeling of Fracture and Failure of Materials and Structures (CFRAC 2023). [http://mech.fsv.cvut.cz/cfrac/abstracts/DDF-351.pdf]
- Poot, A.; Kerfriden, P.; Van der Meer, F.P. (2023). A Bayesian approach to modeling finite element discretization error. 5th International Conference on Uncertainty Quantification in Computational Science and Engineering. [https://arxiv.org/abs/2306.05993v1]
- Riccius, L.; Rocha, I.B.C.M.; Van der Meer, F.P. (2023). Comparison of surrogate modeling and sampling strategies for efficient Bayesian model calibration. 5th International Conference on Uncertainty Quantification in Computational Science and Engineering. [https://2023.uncecomp.org/proceedings/pdf/19832.pdf]
- Storm, J.; Rocha, I.B.C.M.; Van der Meer, F.P. (2023). A Microstructure-based Graph Neural Network for Accelerating Multiscale Simulations. 2nd IACM Mechanistic Machine Learning and Digital Engineering for Computational Science and Engineering Technology. [https://www.morressier.com/o/event/63bee9dba0cfff0012f035b5/article/64c26777632e9539aa87d64b]
- Maia, M.A.; Rocha, I.B.C.M.; Van der Meer, F.P. (2022). Neural networks meet physics-based material models: Accelerating concurrent multiscale simulations of path-dependent composite materials. 20th European Conference on Composite Materials: Composites Meet Sustainability. [https://pure.tudelft.nl/ws/portalfiles/portal/147662267/ECCM2024.pdf]
- Rocha, I.B.C.M; Kerfriden, P.; Van der Meer, F.P. (2022). Machine learning of evolving physics-based material models for fast and accurate concurrent multiscale modeling. 8th European Congress on Computational Methods in Applied Sciences and Engineering. [https://www.eccomas2022.org/admin/files/fileabstract/a1169.pdf]
- Maia, M.A.; Rocha, I.B.C.M.; Kerfriden, P.; Van der Meer, F.P. (2022). Neural networks with embedded physics-based material models to accelerate multiscale finite element simulations. 8th European Congress on Computational Methods in Applied Sciences and Engineering. [https://www.eccomas2022.org/admin/files/fileabstract/a1209.pdf]
- Rocha, I.B.C.M; Kerfriden, P.; Van der Meer, F.P. (2022). Accelerating concurrent multiscale mechanical simulations through physics-infused machine learning of evolving material models. 11th European Solid Mechanics Conference. [https://az659834.vo.msecnd.net/eventsairwesteuprod/production-abbey-public/6d2fca2e67c5414d9deb44eee6f5fe54]
- Rocha, I.B.C.M, Kerfriden, P. and , Van der Meer, F.P. (2021). On-the-fly learning of evolving physics-based material models for concurrent multiscale simulations through state-space modeling with gaussian process dynamics. Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology, San Diego, United States. [https://arxiv.org/abs/2301.13547]
- Rocha, I.B.C.M, Kerfriden, P. and , Van der Meer, F.P. (2021). Comparing Acceleration Techniques for FE2: Classic Mesoscale Modeling, Black-Box Data-Driven Modeling and Physics-Informed Hyper-Reduction. 8th Conference on Mechanical Response of Composites, Gothenburg, Sweden. [https://www.researchgate.net/publication/354317462_Comparing_Acceleration_Techniques_for_FE2_Classic_Mesoscale_Modeling_Black-Box_Data-Driven_Modeling_and_Physics-Informed_Hyper-Reduction]
- Rocha, I.B.C.M, Kerfriden, P. and , Van der Meer, F.P. (2021). Surrogate models for FE2: classic mesoscale modeling, black-box data-driven modeling and physics-informed subspace projection. 14th World Congress in Computational Mechanics, Paris, France. [https://www.scipedia.com/public/Rocha_et_al_2021a]
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