Publications

Anuj Kumar (2024). Accelerating Combustion Simulations with Machine Learning Surrogate Models. PhD Dissertation.

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Anuj Kumar, Tarek Echekki (2024). Combustion chemistry acceleration with DeepONets. In Fuel.

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Arsalan Taassob, Anuj Kumar, Kevin M. Gitushi, Rishikesh Ranade, Tarek Echekki (2024). A PINN-DeepONet framework for extracting turbulent combustion closure from multiscalar measurements. CMA.

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Anuj Kumar, Tarek Echekki (2023). Physics - Informed Machine Learning for Reduced Space Chemical Kinetics. In ML4PS, NeurIPS'23.

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Tadbhagya Kumar, Anuj Kumar, Pinaki Pal (2023). A Physics-Constrained NeuralODE Approach for Robust Learning of Stiff Chemical Kinetics. In ML4PS, NeurIPS'23.

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Ki Sung Jung, Anuj Kumar, Tarek Echekki, Jacqueline H. Chen (2023). On the application of principal component transport for compression ignition of lean fuel/air mixtures under engine relevant conditions. In Combustion and Flame.

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Anuj Kumar, Martin Rieth, Opeoluwa Owoyele, Jacqueline H. Chen, Tarek Echekki (2023). Acceleration of turbulent combustion DNS via principal component transport. In Combustion and Flame.

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Anuj Kumar, Tarek Echekki (2023). A Framework for Combustion Chemistry Acceleration with DeepONets. In arXiv.

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