Data-driven fracture mechanics
Not on the map. Why?
Abstract (arXiv)
We present a new data-driven paradigm for variational brittle fracture mechanics. The fracture-related material modeling assumptions are removed and the governing equations stemming from variational principles are combined with a set of discrete data points, leading to a model-free data-driven method of solution. The solution at a given load step is identified as the point within the data set that best satisfies either the Kuhn-Tucker conditions stemming from the variational fracture problem or global minimization of a suitable energy functional, leading to data-driven counterparts of both the local and the global minimization approaches of variational fracture mechanics. Both formulations are tested on different test configurations with and without noise and for Griffith and R-curve type fracture behavior.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 4; 2023: 2; 2024: 1; 2025: 3; 2026: 1.
13 papers.
Sort:
By area:
- 2021Polyconvex anisotropic hyperelasticity with neural networkscited by 54
- 2024A review on data-driven constitutive laws for solidscited by 35
- 2022NN-EUCLID: deep-learning hyperelasticity without stress datacited by 30
- 2022A physics-informed deep neural network for surrogate modeling in classical elasto-plasticitycited by 14
- 2022Calibrating constitutive models with full-field data via physics informed neural networkscited by 12
- 2025Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs)cited by 12
- 2022Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticitycited by 11
- 2023A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systemscited by 6
- 2025History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materialscited by 5
- 2021Manifold embedding data-driven mechanicscited by 2
- 2025Automating modeling in mechanics: LLMs as designers of physics-constrained neural networks for constitutive modeling of materialscited by 2
- 2023Crack-Net: Prediction of Crack Propagation in Compositescited by 0
- 2026LLM-driven design of physics-constrained constitutive models: two agents are better than one22 May 2026
- 2025Automating modeling in mechanics: LLMs as designers of physics-constrained neural networks for constitutive modeling of materials1 Dec 2025
- 2025History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials12 Jun 2025
- 2025Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs)7 Mar 2025
- 2024A review on data-driven constitutive laws for solids6 May 2024
- 2023Crack-Net: Prediction of Crack Propagation in Composites24 Sep 2023
- 2023A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems26 May 2023
- 2022Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity30 Jul 2022
- 2022NN-EUCLID: deep-learning hyperelasticity without stress data4 May 2022
- 2022A physics-informed deep neural network for surrogate modeling in classical elasto-plasticity26 Apr 2022
- 2022Calibrating constitutive models with full-field data via physics informed neural networks30 Mar 2022
- 2021Manifold embedding data-driven mechanics18 Dec 2021
1 more
- 2021Polyconvex anisotropic hyperelasticity with neural networks20 Jun 2021
- 2021Polyconvex anisotropic hyperelasticity with neural networkscited by 54
- 2024A review on data-driven constitutive laws for solidscited by 35
- 2022NN-EUCLID: deep-learning hyperelasticity without stress datacited by 30
- 2022A physics-informed deep neural network for surrogate modeling in classical elasto-plasticitycited by 14
- 2022Calibrating constitutive models with full-field data via physics informed neural networkscited by 12
- 2025Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs)cited by 12
- 2022Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticitycited by 11
- 2023A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systemscited by 6
- 2025History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materialscited by 5
- 2021Manifold embedding data-driven mechanicscited by 2
- 2023Crack-Net: Prediction of Crack Propagation in Compositescited by 0
- 2025History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials12 Jun 2025
- 2025Can KAN CANs? Input-convex Kolmogorov-Arnold Networks (KANs) as hyperelastic constitutive artificial neural networks (CANs)7 Mar 2025
- 2024A review on data-driven constitutive laws for solids6 May 2024
- 2023Crack-Net: Prediction of Crack Propagation in Composites24 Sep 2023
- 2023A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems26 May 2023
- 2022Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity30 Jul 2022
- 2022NN-EUCLID: deep-learning hyperelasticity without stress data4 May 2022
- 2022A physics-informed deep neural network for surrogate modeling in classical elasto-plasticity26 Apr 2022
- 2022Calibrating constitutive models with full-field data via physics informed neural networks30 Mar 2022
- 2021Manifold embedding data-driven mechanics18 Dec 2021
- 2021Polyconvex anisotropic hyperelasticity with neural networks20 Jun 2021