PINNACLE: PINN Adaptive ColLocation and Experimental Points Selection
📖 What it is
A methods research entry in the AI Singapore research portfolio, PINNACLE proposes an adaptive point-selection method for improving the training efficiency of Physics-Informed Neural Networks (PINNs).
🤖 Relation to AI
Built on empirical Neural Tangent Kernel (NTK) theory, the method jointly and automatically optimizes the selection of all training-point types (experimental and collocation points), significantly outperforming existing benchmarks on forward, inverse, and transfer-learning tasks—an advance at the methods layer where scientific computing meets AI.
🇸🇬 Relation to Singapore
Surfaced through the AI Singapore research portal, PINNACLE reflects Singapore’s investment in fundamental methods research for AI applied to scientific computing (AI4Science).
Sources
- AI Singapore — accessed 2026-07-06
Within 🔬 Foundational Research
A*STAR
Singapore Agency for Science, Technology and Research; primary engine for foundational and applied AI research
NUS
National University of Singapore; among Asia's top AI research universities. Launched the NUS AI Institute in March 2024, bringing together foundational AI, applied AI and societal impact research
NTU
Nanyang Technological University; major hub for AI and data science research
SMU
Singapore Management University; AI applications in business and society
SUTD
Singapore University of Technology and Design; innovation at the intersection of AI and design
AISG Research Collaborative Project with US-NSF Researchers
This collaborative project, led by Professor Jie Zhang (NTU) in partnership with MIT researchers including Associate Professor Jacob Andreas, focuses on developing trustworthy and interpretable human-AI collaborative systems for combinatorial optimization. The research establishes natural language as a robust interface for human-AI interaction, enabling systems to formulate complex optimization problems from imprecise input, perform robust AI-assisted solving, and provide explainable feedback. The outcomes aim to serve industries like logistics, manufacturing, and finance with intelligent decision-making tools, while advancing the theoretical foundations of trustworthy AI collaboration.