Atul Agrawal

Contact

Email: atul.agrawal@tum.de

Publications

Atul Agrawal, Kislaya Ravi, P.S. Koutsourelakis, Hans-Joachim Bungartz: Stochastic Black-Box Optimization using Multi-Fidelity Score Function Estimator (Under Review)

Atul Agrawal, Erik Tamsen, Phaedon-Stelios Koutsourelakis, Joerg F. Unger: From concrete mixture to structural design -- a holistic optimization procedure in the presence of uncertainties (Data Centric Engineering, 2024)

Atul Agrawal, Phaedon-Stelios Koutsourelakis: A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty (Journal of Computational Physics, 2024)

Atul Agrawal, Kislaya Ravi, P.S. Koutsourelakis, Hans-Joachim Bungartz: Multi-fidelity Constrained Optimization for Stochastic Black-Box Simulators (NeurIPS 2023, Machine learning for Physical Sciences)

Leon Riccius, Atul Agrawal, P.S. Koutsourelakis: Physics-Informed Tensor Basis Neural Network for Turbulence Closure Modeling (NeurIPS 2023, Machine learning for Physical Sciences)

Didier Lucor, Atul Agrawal, Anne Sergent: Simple computational strategies for more effective physics-informed neural networks modeling of turbulent natural convection  (Journal of Computational Physics, 2022)

Singh, D., Agrawal, A., and Roy Mahapatra, D., A Reduced-Order Modeling Framework for Simulating Signatures of Faults in a Bladed Disk, SAE Int. J. Aerosp.

Conference

NeurIPS 2023	2023	poster 1, poster 2, poster 3 (Machine Learning for Physical Sciences Workshop)
WCCM 2022 SIAM UQ 2022 FrontUQ ENUMATH ISSS	2022 2022 2019: 2019 2017	Inverting the Process-Material-Performance chain in the presence of uncertainty A Probabilistic, Data-Driven Closure Model for Rans Simulations and Model Uncertainty Investigation of PDE-constrained deep neural networks for efficient flow field assimilation PDE-constrained neural network for turbulent Rayleigh-Bénard convection Reduced order modeling framework of damage detection in bladed disk

Workshops and Seminars

• Differentiable and Probabilistic Programming in Fundamental Physics (2023, Garching GERMANY)
• Nordic Probabilistic AI school 2021 (Trondheim, Norway)
• Summer school of Machine Learning 2020, Skoltech, Moscow Poster Presenter

Teaching

• Uncertainty Modeling in Engineering (WS 2023 -)
• Modeling in Structural Mechanics ( SS 2021 - )
• Continuum Mechanics (SS 2020 - )

Supervised Student Projects

• Mohammad Anas Khan (Master's Thesis) : Black-Box Optimization for Engineering Systems with Score-Function Estimator. Here (Supervised together with Kislaya Ravi)
• Jonas Eichelsdörfer (Master's Thesis) : Physics Informed Machine Learning and Hamiltonian Neural Networks ( supervised together with Sebastian Kaltenbach)
• Leon Riccius (Master's Thesis): Machine Learning based approach for investigating Reynolds stress discrepancy based on DNS/LES data. Here

Open Student Projects :

• Developing differentiable RANS solver in PyTorch for downstream UQ/ML tasks (details here)

Education

• 2017-2019: M.Sc. Computational Mechanics, Ecole Centrale Nantes
  • Master's Thesis: Surrogate Modelling for Turbulent Thermal Convection Processes Based on Physics Informed Deep Learning ,(LIMSI-CNRS, Universite paris Sacalay, Orsay FRANCE)
• 2010-2014: B.Tech. Mechanical Engineering, Indian Institute of Technology, Varanasi (IIT-BHU)