11 papers
AI-Driven Performance-to-Design Generation and Optimization of Marine Propellers
Leah Chen, Keni Chih-Hua Wu, Boon Tat Chia +2
AI is increasingly used to accelerate engineering design by improving decision-making and shortening iteration cycles. Application to marine propeller design, however, remains chal…
Agentic AI-Enabled Framework for Thermal Comfort and Building Energy Assessment in Tropical Urban Neighborhoods
Po-Yen Lai, Xinyu Yang, Derrick Low +2
In response to the urban heat island effects and building energy demands in Singapore, this study proposes an agentic AI-enabled reasoning framework that integrates large language…
Transferable Physics-Informed Representations via Closed-Form Head Adaptation
Jian Cheng Wong, Isaac Yin Chung Lai, Pao-Hsiung Chiu +3
Physics-informed neural networks (PINNs) have garnered significant interest for their potential in solving partial differential equations (PDEs) that govern a wide range of physica…
FFV-PINN: A Fast Physics-Informed Neural Network with Simplified Finite Volume Discretization and Residual Correction
Chang Wei, Yuchen Fan, Jian Cheng Wong +3
Physics-informed neural networks (PINNs) have emerged as a major research focus. However, today's PINNs encounter several limitations. Firstly, during the construction of the loss…
Bridging Computational Fluid Dynamics Algorithm and Physics-Informed Learning: SIMPLE-PINN for Incompressible Navier-Stokes Equations
Chang Wei, Yuchen Fan, Chin Chun Ooi +3
Physics-informed neural networks (PINNs) have shown promise for solving partial differential equations (PDEs) by directly embedding them into the loss function. Despite their notab…
Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction
Pao-Hsiung Chiu, Jian Cheng Wong, Chin Chun Ooi +3
Physics-informed neural networks (PINNs) have emerged as a promising mesh-free paradigm for solving partial differential equations, yet adoption in science and engineering is limit…