SPARK Lab
SCIENTIFIC PREDICTION THROUGH AI RESEARCH & KNOWLEDGE
Department of Computer Science · Texas State University · San Marcos, TX
About SPARK
The SPARK Lab (Scientific Prediction through AI Research & Knowledge) develops next-generation AI methods that are grounded in scientific principles. We build machine learning algorithms that don’t just fit data — they respect the laws of physics, scale to real-world complexity, and provide interpretable insights for scientific discovery.
Our work spans physics-informed neural networks, neural operators, generative AI for science, and hybrid modeling — with applications ranging from climate modeling and turbulence to nanoscale heat conduction and metamaterial design.
Research Areas
Scientific Machine Learning
Physics-Informed Neural Networks (PINNs), DeepONets, and deep neural operators for solving complex PDEs and multiphysics problems.
Climate & Earth System Modeling
Neural operator-based bias corrections, nudging strategies for E3SM, and hybrid approaches for weather and climate prediction.
Turbulence & Fluid Dynamics
Generative models and diffusion-based neural operators for super-resolution, forecasting, and sparse reconstruction of turbulent flows.
Nanoscale Heat Conduction
Neural network methods for ultrashort-pulsed laser heating, parabolic two-temperature models, and multi-layer thin film thermal analysis.
Neural Operators & Spectral Methods
Mitigating spectral bias, high-frequency scaling, multi-fidelity operator learning for physical systems.
Engineering & Inverse Design
MOSFET heat sink optimization, PIER routing, mechanical metamaterial characterization, and inverse design via neural operators.
Team
Dr. Aniruddha Bora
Open Position
See details below.
Open Position
to reach out.
Past Mentees
- Sotos Lois (Imperial College London, 2022–2023) — Mathematical finance using PINNs and operator learning
Selected Publications
Collaborators
Grants & Funding
- PIER: Physics-Informed, Energy-efficient, Risk-aware Routing — Texas State University, $12,000 (2026–Present)
- ALCF Director’s Discretionary Allocation — Physics-Informed Generative AI (Argonne)
- ALCF Director’s Discretionary Allocation — Extreme Weather via Neural Operator Approximation (Argonne)
- MURI Program (ONR) — ML Methods for Phase Change Heat Transfer Modeling and Design (Brown, contributor)
HPC Resources
The SPARK Lab has access to world-class computing infrastructure:
- ALCF Polaris — HPE Cray EX (AMD EPYC + NVIDIA A100)
- ALCF Aurora — HPE Cray EX (Intel Sapphire Rapids + Intel Data Center GPU Max)
- OSCAR — Brown University HPC Cluster
🔥 Join the SPARK Lab!
I am recruiting one funded Ph.D. student for Fall 2026 and welcome motivated Master's and undergraduate researchers.
Looking for students with backgrounds in CS, Applied Math, Physics, or Engineering interested in:
Machine learning for physical systems · Scientific & interpretable AI · Computational modeling using AI
📧 Apply Now — aniruddha_bora@txstate.eduContact
Dr. Aniruddha Bora
Department of Computer Science
310D COMAL, Texas State University
San Marcos, TX 78666
📧 aniruddha_bora@txstate.edu
🌐 aniruddhabora.github.io
🔗 LinkedIn
📚 Google Scholar