4 papers
GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics
Suguru Shiratori, Elham Kiyani, Khemraj Shukla +1
We develop a data-driven framework for discovering constitutive relations in models of fluid flow and scalar transport. Under the assumption that velocity and/or scalar fields are…
Crack Path Prediction with Operator Learning using Discrete Particle System data Generation
Elham Kiyani, Venkatesh Ananchaperumal, Ahmad Peyvan +3
Accurately modeling crack propagation is critical for predicting failure in engineering materials and structures, where small cracks can rapidly evolve and cause catastrophic damag…
Predicting Crack Nucleation and Propagation in Brittle Materials Using Deep Operator Networks with Diverse Trunk Architectures
Elham Kiyani, Manav Manav, Nikhil Kadivar +2
Phase-field modeling reformulates fracture problems as energy minimization problems and enables a comprehensive characterization of the fracture process, including crack nucleation…
PINNs4Drops: Video-conditioned physics-informed neural networks for two-phase flow reconstruction
Maximilian Dreisbach, Elham Kiyani, Jochen Kriegseis +2
Two-phase flow phenomena underpin critical technologies such as hydrogen fuel cells, spray cooling, and combustion, where droplet dynamics govern performance and efficiency. Conven…