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Research in the SML community is multi-faceted, and encompasses research in the fundamental development of SML algorithms and approaches as well as application-specific research in various scientific domains. The TAMU SML community is an aggregation of researchers from computer science, electrical engineering, aerospace engineering, physics and astronomy, petroleum engineering, atmospheric sciences, and many others. Combine this breadth of knowledge with the support of researchers and staff from the Texas A&M High Performance Research Center (TAMU HPRC) and a rich and vibrant community of collaborative and interdisciplinary research is born.

Recent Papers and Preprints


Y. Wang, X. Han, C.Y. Chang, D. Zha, U. Braga-Neto and X. Hu, “Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture.” arXiv preprint arXiv:2205.13748

E.J.R. Coutinho, M. Dall’Aqua, L. McClenny, M. Zhong, U. Braga-Neto and E. Gildin, “Physics-Informed Neural Networks with Adaptive Localized Artificial Viscosity”. arXiv preprint arXiv:2203.08802

C. Davi and U. Braga-Neto, “PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization”. arXiv preprint arXiv:2202.01943


L. McClenny, M. Haile, and U. Braga-Neto, “TensorDiffEq: Scalable Multi-GPU Forward and Inverse Solvers for Physics Informed Neural Networks.” arXiv preprint arXiv:2103.16034

L. McClenny and U. Braga-Neto, “Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism,” AAAI Spring Symposium on Combining Intelligence and Machine Learning with Physics Sciences (AAAI-MLPS), March 22-24, 2021.

V. Deshpande, R. Bhattacharya, D. Donzis, “A Unified Framework to Generate Optimized Compact Finite Difference Schemes,” Journal of Computational Physics, 2021.

E.J. Coutinho, E. Gildin, and M.J. Dall’Aqua, “Physics-aware Deep-learning-based Proxy Reservoir Simulation Model Equipped With State And Well Output Prediction.” SPE Reservoir Simulation Conference. March 1-3, 2021, Texas, USA.


V. Deshpande, R. Bhattacharya, “Surrogate Modeling of Dynamics From Sparse Data Using Maximum Entropy Basis Functions,” American Control Conference, 2020.

V. Deshpande, R. Bhattacharya, “Data-driven Solution of Stochastic Differential Equations Using Maximum Entropy Basis Functions,” IFAC World Congress, 2020.

Cheung, S.W., Chung, E.T., Efendiev, Y. et al. “Deep global model reduction learning in porous media flow simulation.” Comput Geosci 24, 261–274, 2020.

H. Florez and E. Gildin, “Global/local model order reduction in coupled flow and linear thermal-poroelasticity.” Comput Geosci 24, 709–735, 2020.

C. Kunselman, V. Attari, L. McClenny, U. Braga-Neto and R. Arroyave, “Semi-supervised Learning Approaches to Class Assignment in Ambiguous Microstructures,” Acta Materialia, Vol. 188, Apr 2020, pp. 49-62.