2 citations · 4 across the 4 of their papers we have counts for
4 papers
ML-based Method for Solving the Microkinetic Model of Fischer-Tropsch Synthesis with Varying Catalyst/Reactor Parameters
Taras Demchuk, Tymofii Nikolaienko, Aniruddha Panda +3
This study introduces a physics-informed machine learning framework to accelerate the computation of the microkinetic model of Fischer-Tropsch synthesis. A neural network, trained…
Physics-informed neural networks need a physicist to be accurate: the case of mass and heat transport in Fischer-Tropsch catalyst particles
Tymofii Nikolaienko, Harshil Patel, Aniruddha Panda +3
Physics-Informed Neural Networks (PINNs) have emerged as an influential technology, merging the swift and automated capabilities of machine learning with the precision and dependab…
Large Language Model-Based Evolutionary Optimizer: Reasoning with elitism
Shuvayan Brahmachary, Subodh M. Joshi, Aniruddha Panda +6
Large Language Models (LLMs) have demonstrated remarkable reasoning abilities, prompting interest in their application as black-box optimizers. This paper asserts that LLMs possess…
Accurate and Fast Fischer-Tropsch Reaction Microkinetics using PINNs
Harshil Patel, Aniruddha Panda, Tymofii Nikolaienko +3
Microkinetics allows detailed modelling of chemical transformations occurring in many industrially relevant reactions. Traditional way of solving the microkinetics model for Fische…