Multiscale modeling meets machine learning: What can we learn?

• Predictive modeling in biology and medicine: Digital twins and multi-scale modeling

  2026 PLoS Computational Biology article Biochemistry, Genetics and Molecular Biology Gene Regulatory Network Analysis Open access

  0

  citations
• Finite Operator Learning: Bridging neural operators and numerical methods for efficient parametric solution and optimization of PDEs

  2025 Finite Elements in Analysis and Design article Physics and Astronomy Model Reduction and Neural Networks

  10

  citations
• Discovering uncertainty: Bayesian constitutive artificial neural networks

  2024 Computer Methods in Applied Mechanics and Engineering article Computer Science Gaussian Processes and Bayesian Inference Open access

  37

  citations
• Easy-MODA: Simplifying standardised registration of scientific simulation workflows through MODA template guidelines powered by the Enalos Cloud Platform

  2024 Computational and Structural Biotechnology Journal article Decision Sciences Scientific Computing and Data Management Open access

  13

  citations
• Discovering uncertainty: Bayesian constitutive artificial neural networks

  2024 bioRxiv (Cold Spring Harbor Laboratory) preprint Computer Science Gaussian Processes and Bayesian Inference Open access

  6

  citations
• Automated model discovery for human cardiac tissue: Discovering the best model and parameters

  2024 Computer Methods in Applied Mechanics and Engineering article Engineering Elasticity and Material Modeling Open access

  46

  citations
• Automated model discovery for human cardiac tissue: Discovering the best model and parameters

  2024 bioRxiv (Cold Spring Harbor Laboratory) preprint Engineering Elasticity and Material Modeling Open access

  6

  citations
• Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks

  2024 Computational Mechanics article Physics and Astronomy Model Reduction and Neural Networks

  82

  citations
• Mixed formulation of physics‐informed neural networks for thermo‐mechanically coupled systems and heterogeneous domains

  2023 International Journal for Numerical Methods in Engineering article Physics and Astronomy Model Reduction and Neural Networks Open access

  98

  citations
• A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: Comparison with finite element method

  2022 Computer Methods in Applied Mechanics and Engineering article Physics and Astronomy Model Reduction and Neural Networks Open access

  12

  citations
• Machine-learning-based surrogate modeling of microstructure evolution using phase-field

  2022 Computational Materials Science article Materials Science Machine Learning in Materials Science

  62

  citations
• A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method

  2022 arXiv (Cornell University) preprint Physics and Astronomy Model Reduction and Neural Networks Open access

  216

  citations
• Machine learning-based structure–property predictions in silica aerogels

  2021 Soft Matter article Materials Science Machine Learning in Materials Science

  26

  citations

13 results