8 papers
Physics-Audited Agentic Discovery in Scientific Machine Learning
Diab W. Abueidda, Bilal Ahmed, Panos Pantidis +1
In agentic scientific machine learning (SciML), large language model (LLM) agents can discover surrogate models and select one by an automated score, typically an error metric. A l…
A hybrid IFENN solver for generalizable modeling of phase-field fracture initiation and propagation
Panos Pantidis, Fouad Amin, Diab Abueidda +1
In this paper we demonstrate how the Integrated Finite Element Neural Network (IFENN) framework can effectively model the entire evolution of phase-field fracture, including the in…
I-FENN with DeepONets: accelerating simulations in coupled multiphysics problems
Fouad M. Amin, Diab W. Abueidda, Panos Pantidis +1
Coupled multiphysics simulations for high-dimensional, large-scale problems can be prohibitively expensive due to their computational demands. This article presents a novel framewo…
Time Resolution Independent Operator Learning
Diab W. Abueidda, Mbebo Nonna, Panos Pantidis +1
Accurately learning solution operators for time-dependent partial differential equations (PDEs) from sparse and irregular data remains a challenging task. Recurrent DeepONet extens…
Modified non-local damage model: resolving spurious damage evolution
Roshan Philip Saji, Panos Pantidis, Mostafa E. Mobasher
Accurate prediction of damage and fracture evolution is critical for the safety design and preventive maintenance of engineering structures, however existing computational methods…
Variational PINNs with tree-based integration and boundary element data in the modeling of multi-phase architected materials
Dimitrios C. Rodopoulos, Panos Pantidis, Nikolaos Karathanasopoulos
The current contribution develops a Variational Physics-Informed Neural Network (VPINN)-based framework for the analysis and design of multiphase architected solids. The elaborated…