Physics-informed learning
Learning methods that respect physical laws, combining differential equations and data to model complex systems.
Assistant Professor · Computer Science
Aniruddha
Bora.
AI for a world
governed by physics.
governed by physics.
I connect machine learning, applied mathematics, and scientific computing to understand and model complex physical systems.
AN EXPLORATION OF SCIENTIFIC SYSTEMS
PHYSICS × LEARNING × DISCOVERY
A CONCEPTUAL SCIENTIFIC FIELD
VORTEX · FLOW THROUGH A THREE-DIMENSIONAL FIELD
MOVE TO EXPLORE · TAP TO SEND A PULSE
CONCEPTUAL VISUALIZATION · NOT RESEARCH DATA
Texas State UniversityDepartment of Computer Science
Principal Investigator · SPARKS LabScientific AI, grounded in domain knowledge
SAN MARCOS, TEXAS
29.8833° N / 97.9414° W
29.8833° N / 97.9414° W
Research philosophy
Physical insight. Computational intelligence.
Generative AI for science
Generative models that recover fine-scale structure and improve scientific prediction, from turbulent flows to climate.
Scientific discovery & decision-making
Interpretable, data-efficient AI and autonomous decision-making through reinforcement learning, agentic systems, and physical knowledge.
Selected research
From equations to discovery.
Resolving the missing scales of turbulence
Combining neural operators and diffusion models to recover high-frequency flow structures.
Explore the research ↗
Bringing learned corrections into Earth systems
Online neural operator updates for stable, multiyear hybrid climate simulations.
Explore the research ↗Recent scholarship
Ideas in print.
Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction
Nature Communications Journal article
Inception UNet (IUNet) and Multi-scale Multi-branch (M&M) UNet Plug-In Neural Operators for Online Bias Correction in E3SM.
Journal of Computational Physics Journal article
Journal article · 2026; link to earlier preprint
Improved nudging strategy for constraining humidity and temperature fields in the E3SM Atmosphere Model.
Geoscientific Model Development Journal article
Integrating neural operators with diffusion models improves spectral representation in turbulence modeling
Proceedings of the Royal Society A Journal article
About me
Working at the intersection
of disciplines.
I am an Assistant Professor of Computer Science at Texas State University, where I lead SPARKS Lab. My work brings together ideas from computer science, applied mathematics, and scientific computing.
Before joining Texas State, I was a Postdoctoral Research Associate at Brown University. I earned my Ph.D. in Computational Analysis and Modeling from Louisiana Tech University in 2021. In Summer 2026, I was a J. Tinsley Oden Faculty Fellow at UT Austin.
My focus is on robust, interpretable learning algorithms that combine physical insight with modern AI.
Research, recognition & impact
Beyond the publication.
DOE Genesis · Phase I
Co-PI and Texas State subaward PI for agentic AI in Earth-system prediction, with Pacific Northwest National Laboratory.
Funding & collaborations ↗Research into practice
Patent applications in laser control, maritime routing, traffic signals, and adaptive waste collection.
Innovation & intellectual property ↗Connecting disciplines
Oden Faculty Fellow, BioNTX Showcase winner, and forthcoming SIAM minisymposium co-organizer.
Service & leadership ↗Connect
Good science starts
with a conversation.
Interested in research collaboration or joining SPARKS Lab? I welcome conversations across computer science, mathematics, physics, and engineering.
aniruddha_bora@txstate.edu ↗
Department of Computer Science · Texas State University
310D Comal · San Marcos, Texas 78666