F. P. van der Meer
Orcid: 0000-0002-6691-1259Affiliations:
- Delft University of Technology, Faculty of Civil Engineering and Geosciences, The Netherlands
According to our database1,
F. P. van der Meer
authored at least 16 papers
between 2021 and 2025.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2025
Mixing Data-Driven and Physics-Based Constitutive Models using Uncertainty-Driven Phase Fields.
CoRR, April, 2025
A viscoplasticity model with an invariant-based non-Newtonian flow rule for unidirectional thermoplastic composites.
CoRR, April, 2025
Uncertainty Quantification in Multiscale Modeling of Polymer Composite Materials Using Physically Recurrent Neural Networks.
CoRR, April, 2025
Effects of Interpolation Error and Bias on the Random Mesh Finite Element Method for Inverse Problems.
CoRR, April, 2025
Surrogate-based multiscale analysis of experiments on thermoplastic composites under off-axis loading.
CoRR, January, 2025
2024
Stat. Comput., October, 2024
Physically Recurrent Neural Networks for Computational Homogenization of Composite Materials with Microscale Debonding.
CoRR, 2024
Physically recurrent neural network for rate and path-dependent heterogeneous materials in a finite strain framework.
CoRR, 2024
Modeling of progressive high-cycle fatigue in composite laminates accounting for local stress ratios.
CoRR, 2024
CoRR, 2024
2023
A numerical framework for simulating progressive failure in composite laminates under high-cycle fatigue loading.
CoRR, 2023
Machine learning of evolving physics-based material models for multiscale solid mechanics.
CoRR, 2023
2022
Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate.
CoRR, 2022
Data presented in the paper: "Microscale modeling of rate-dependent failure in thermoplastic composites under off-axis loading".
Dataset, 2022
2021
On-the-fly construction of surrogate constitutive models for concurrent multiscale mechanical analysis through probabilistic machine learning.
J. Comput. Phys. X, 2021