Scientific Computing and Intelligence Group (Scaling Group) is in the Department of Mathematics at National University of Singapore, led by Dr. Liu Yang (杨柳).
Our work centers on In-Context Operator Networks (ICON), a foundation-model framework for scientific problems. Through this lens, we study directions including multi-physics prediction, physical modeling, and embodied AI. To accelerate our research, we also develop agents (EvE) for automated discovery of algorithms and beyond.
We are looking for passionate new students, postdocs, and visiting scholars to join the team!
Jun 18 2026
Our paper Agentic Symbolic Search: Characterizing PDEs Beyond Hand-crafted Expressions, Meshes, and Neural Networks is now available on arXiv.
Jun 10 2026
Our paper Harness In-Context Operator Learning with Chain of Operators is now available on arXiv.
Jun 10 2026
Our paper VICX: Generalizable Robot Manipulation via Video Generation and In-Context Operator Network is now available on arXiv.
Jun 07 2026
Our paper Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control is now available on arXiv.
May 09 2026
Our paper Evolutionary Ensemble of Agents (GitHub) is now available on arXiv. We introduce EvE: a decentralized evolutionary ensemble of coding agents co-evolving with code repositories.
Apr 28 2026
Our paper In-context modeling as a retrain-free paradigm for foundation models in computational science is now available on arXiv.
Apr 25 2026
Our paper Escher-Loop: Mutual Evolution by Closed-Loop Self-Referential Optimization is now available on arXiv.
Mar 14 2026
We released icon-core, an open-source research infrastructure package for In-Context Operator Network (ICON) development. It provides standard models and algorithms, benchmark datasets, training pipelines, and development utilities, featuring built-in support for AI coding assistants like Claude Code.
Mar 13 2026
Our paper Graph In-Context Operator Networks for Generalizable Spatiotemporal Prediction is now available on arXiv.
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