Tapas Tripura
Indian Institute of Technology Delhi, Indian Institute of Technology Guwahati
0000-0003-0363-2663
About
The author, Tapas Tripura, was born in Sabroom, a town in South Tripura district, Tripura, India, to Mr. Sudhir Tripura and Mrs. Anjana Tripura. He completed his schooling at Sabroom Higher Secondary Boys’ School while growing up in the Public Works Department (PWD) residential quarters, where he stayed with his father.
He received his B.Tech. degree in Civil Engineering from the North Eastern Regional Institute of Science and Technology (NERIST) in 2016, where he was awarded the Gold Medal in Civil Engineering for outstanding academic performance. He subsequently obtained his M.Tech. degree in Structural Engineering from the Indian Institute of Technology Guwahati in 2019, and received the Best Master’s Thesis Award for his thesis work. He pursued his doctoral research in the Department of Applied Mechanics at the Indian Institute of Technology Delhi and was affiliated with the Center for Scientific Computing and Computational Mechanics (CSCCM). He successfully defended his Ph.D. thesis on May 26, 2026.
His research interests lie at the intersection of computational mechanics, scientific machine learning, and artificial intelligence. His major contributions focus on data-driven discovery of physical systems, operator learning for accelerating computational simulations, scientific foundation modeling, and reinforcement learning for intelligent control of mechanical systems. During his doctoral studies, he has contributed to the development of the Wavelet Neural Operator (WNO), an interpretable Lagrangian discovery framework for directly learning governing equations from data, the Neural Compositional Wavelet Neural Operator (NCWNO) as a scientific foundation model, and the Model-Agnostic Predictive Temporal Difference (MAP-TD) for model-based deep reinforcement learning. During his doctoral studies, he published 23 journal articles in Q1-ranked international journals, one book chapter, and six conference papers.
He is also a recipient of the Prime Minister’s Research Fellowship (PMRF), awarded by the Ministry of Education, Government of India.
His long-term research interests lie in bridging physics-based modeling and machine learning for complex physical systems, particularly by integrating planning and reasoning into sequential decision-making frameworks such as deep reinforcement learning, and by developing learning frameworks for stochastic dynamical systems.
Employment
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Indian Institute of Technology Delhi Project Associate2026 - 2026
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Indian Institute of Technology Guwahati Assistant Project Engineer2019 - 2020
Education
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Indian Institute of Technology Delhi Doctoral Student2021 - 2026
Projects & Funding
Projects & funding information is unavailable.
Publications (51)
- Learning to predict and control with sparse model discovery and deep temporal difference reinforcement learning Save
- From local interactions to global operators: Scalable Gaussian process operator for physical systems Save
- Neural combinatorial wavelet neural operator for catastrophic forgetting free in-context operator learning of multiple partial differential equations Save
- Deep muscle electromyogram construction using a physics-integrated deep learning approach Save
- Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator Save
- DEEP MUSCLE EMG CONSTRUCTION USING A PHYSICS-INTEGRATED DEEP LEARNING APPROACH Save
- FROM LOCAL INTERACTIONS TO GLOBAL OPERATORS: SCALABLE GAUSSIAN PROCESS OPERATOR FOR PHYSICAL SYSTEMS Save
- Generative adversarial wavelet neural operator with applications to fault detection and isolation of multivariate time series data Save
- Discovering stochastic partial differential equations from limited data using variational Bayes inference Save
- Physics informed WNO Save