94 citations · 146 across the 16 of their papers we have counts for
16 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…
PINEAPPLE: Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter Inference in Lithium-Ion Battery Electrodes
Karkulali Pugalenthi, Jian Cheng Wong, Qizheng Yang +4
Accurate, real-time, yet non-destructive estimation of internal states in lithium-ion batteries is critical for predicting degradation, optimizing usage strategies, and extending o…
Landscape-aware Automated Algorithm Design: An Efficient Framework for Real-world Optimization
Haoran Yin, Shuaiqun Pan, Zhao Wei +5
The advent of Large Language Models (LLMs) has opened new frontiers in automated algorithm design, giving rise to numerous powerful methods. However, these approaches retain critic…
Out-of-Distribution Generalization for Neural Physics Solvers
Zhao Wei, Chin Chun Ooi, Jian Cheng Wong +3
Neural physics solvers are increasingly used in scientific discovery, given their potential for rapid in silico insights into physical, materials, or biological systems and their l…
Evolvable Conditional Diffusion
Zhao Wei, Chin Chun Ooi, Abhishek Gupta +4
This paper presents an evolvable conditional diffusion method such that black-box, non-differentiable multi-physics models, as are common in domains like computational fluid dynami…
Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities
Jian Cheng Wong, Abhishek Gupta, Chin Chun Ooi +3
Deep learning models trained on finite data lack a complete understanding of the physical world. On the other hand, physics-informed neural networks (PINNs) are infused with such k…
Tailoring Generative Adversarial Networks for Smooth Airfoil Design
Joyjit Chattoraj, Jian Cheng Wong, Zhang Zexuan +8
In the realm of aerospace design, achieving smooth curves is paramount, particularly when crafting objects such as airfoils. Generative Adversarial Network (GAN), a widely employed…
Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin Effect
Jian Cheng Wong, Chin Chun Ooi, Abhishek Gupta +4
Physics-informed neural networks (PINNs) are at the forefront of scientific machine learning, making possible the creation of machine intelligence that is cognizant of physical law…
Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design
Jian Cheng Wong, Chin Chun Ooi, Joyjit Chattoraj +6
Computational Intelligence (CI) techniques have shown great potential as a surrogate model of expensive physics simulation, with demonstrated ability to make fast predictions, albe…
Neuroevolution of Physics-Informed Neural Nets: Benchmark Problems and Comparative Results
Nicholas Sung Wei Yong, Jian Cheng Wong, Pao-Hsiung Chiu +3
The potential of learned models for fundamental scientific research and discovery is drawing increasing attention worldwide. Physics-informed neural networks (PINNs), where the los…
Learning in Sinusoidal Spaces with Physics-Informed Neural Networks
Jian Cheng Wong, Chinchun Ooi, Abhishek Gupta +1
A physics-informed neural network (PINN) uses physics-augmented loss functions, e.g., incorporating the residual term from governing partial differential equations (PDEs), to ensur…