Conferences

International

Shilaj Baral, Youngkyu Lee, Sangam Khanal, and Joongoo Jeon. "Enabling long-term simulation of 3D natural circulation through a residual-guided AI-CFD hybrid method", Pacific Basin Nuclear Conference, Nuclear Energy for an AI-powered world, Busan, Korea, April 22-24, 2026. (Oral)
Shilaj Baral, Youngkyu Lee, Sangam Khanal, and Joongoo Jeon. "How to achieve robust CFD acceleration with scientific machine learning", 21^th International Topical Meeting on Nuclear Reactor Thermal Hydraulics, Busan, Korea, August 31-September 5, 2025. (Oral)
Shilaj Baral, Sangam Khanal, and Joongoo Jeon. "Introduction to the RePIT project: A hybrid method for accelerating long-term CFD simulations via deep learning", The 13^th Korea-Japan Symposium on Nuclear Thermal Hydraulics and Safety, Seoul, Korea, November 10-13, 2024. (Poster)

Shilaj Baral, Youngkyu Lee, Sangam Khanal, and Joongoo Jeon. "Residual-guided AI-CFD automated framework can accelerate 3D fluid flows: A natural circulation case study", Korean Nuclear Society Spring Conference, Jeju, Korea, May 06-08, 2026. (Oral)
Shilaj Baral, Youngkyu Lee, Sangam Khanal, and Joongoo Jeon. "Reducing computational time for 3D natural circulation via AI-CFD hybrid method.", 2026년 한국전산유체공학회 춘계학술대회, Jecheon, Korea, April 08-10, 2026. (Oral)
Shilaj Baral, Youngkyu Lee, Sangam Khanal, and Joongoo Jeon. "RePIT-v1.0: A software for hybrid ML-CFD computation", Transactions of the Korean Nuclear Society Spring Meeting, Jeju, Korea, May 22-23, 2025. (Oral)
Shilaj Baral, Youngkyu Lee, Sangam Khanal, and Joongoo Jeon. "RePIT-v1.0: A software for hybrid ML-CFD computation", Transactions of the Korean Nuclear Society Spring Meeting, Jeju, Korea, May 22-23, 2025. (Oral)

Presented on JK-FLOW. Title: Scalability and generalization of residual-guided ML-CFD hybrid simulation: From 2D benchmarks to 3D application