An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data. - Bahador Bahmani; WaiChing Sun (CoRR, 2020)

Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph. - Chen Cai; Nikolaos Vlassis; Lucas Magee; Ran Ma; Zeyu Xiong; Bahador Bahmani; Teng-Fong Wong; Yusu Wang 001; WaiChing Sun (CoRR, 2021)

Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation. - Xiao Sun; Bahador Bahmani; Nikolaos N. Vlassis; WaiChing Sun; Yanxun Xu (CoRR, 2021)

Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings. - Bahador Bahmani; WaiChing Sun (CoRR, 2021)

Manifold embedding data-driven mechanics. - Bahador Bahmani; WaiChing Sun (CoRR, 2021)

Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions. - Bahador Bahmani; Hyoung Suk Suh; WaiChing Sun (CoRR, 2023)

Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials. - Bahador Bahmani; WaiChing Sun (CoRR, 2023)

A review on data-driven constitutive laws for solids. - Jan Niklas Fuhg; Govinda Anantha Padmanabha; Nikolaos Bouklas; Bahador Bahmani; WaiChing Sun; Nikolaos N. Vlassis; Moritz Flaschel; Pietro Carrara; Laura De Lorenzis (CoRR, 2024)

A Resolution Independent Neural Operator. - Bahador Bahmani; Somdatta Goswami; Ioannis G. Kevrekidis; Michael D. Shields (CoRR, 2024)

Geometry-Informed Data-Driven Mechanics - Bahador Bahmani (2024)

Neural chaos: A spectral stochastic neural operator. - Bahador Bahmani; Ioannis G. Kevrekidis; Michael D. Shields (J. Comput. Phys., 2025)