Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Kalyanaraman, Kaushic"'
Autor:
Nikolaienko, Tymofii, Patel, Harshil, Panda, Aniruddha, Joshi, Subodh Madhav, Jaso, Stanislav, Kalyanaraman, Kaushic
Physics-Informed Neural Networks (PINNs) have emerged as an influential technology, merging the swift and automated capabilities of machine learning with the precision and dependability of simulations grounded in theoretical physics. PINNs are often
Externí odkaz:
http://arxiv.org/abs/2411.10048
Autor:
Brahmachary, Shuvayan, Joshi, Subodh M., Panda, Aniruddha, Koneripalli, Kaushik, Sagotra, Arun Kumar, Patel, Harshil, Sharma, Ankush, Jagtap, Ameya D., Kalyanaraman, Kaushic
Large Language Models (LLMs) have demonstrated remarkable reasoning abilities, prompting interest in their application as black-box optimizers. This paper asserts that LLMs possess the capability for zero-shot optimization across diverse scenarios, i
Externí odkaz:
http://arxiv.org/abs/2403.02054
Autor:
Patel, Harshil, Panda, Aniruddha, Nikolaienko, Tymofii, Jaso, Stanislav, Lopez, Alejandro, Kalyanaraman, Kaushic
Microkinetics allows detailed modelling of chemical transformations occurring in many industrially relevant reactions. Traditional way of solving the microkinetics model for Fischer-Tropsch synthesis (FTS) becomes inefficient when it comes to more ad
Externí odkaz:
http://arxiv.org/abs/2311.10456