AI for Science Conference
The topics of interest for submission include, but are not limited to:
Scientific machine learning；
Physics-, chemistry-, and biology-informed learning；
Causality, mechanistic models, symbolic AI & neuro-symbolic reasoning；
Uncertainty quantification, calibration, robust & trustworthy AI；
Explainability, interpretability, and scientific hypothesis generation；
Surrogate modeling for simulators；
Differentiable programming & PDE solvers；
Foundation models for scientific data (text, code, graphs, time series, multimodal)；
Data management & curation；
Lab automation; autonomous agents for experiments；
HPC, distributed training, accelerators, and efficient AI at scale；
Benchmarking, evaluation protocols, and open-source toolchains；
Scientific visualization, human-in-the-loop systems, and interactive assistants；
Physics, astronomy, cosmology, HEP/NP, plasma & fusion, materials science；
Inverse problems, instrument control, detector/beamline optimization；
Quantum science & technology；
Quantum-inspired ML；
Generative design and retrosynthesis；
Molecular property prediction；
Reaction networks, catalysis, battery & energy materials discovery；
Multiscale modeling, electronic structure, and spectroscopy analysis；
Genomics, proteomics, structural biology, and systems biology；
Bioinformatics, single-cell & spatial omics；
Drug discovery and design；
Neuroscience & cognitive science；
Biomedical imaging and digital twins；
Climate modeling, weather prediction, hydrology, and oceanography；
Remote sensing, GIS, geophysics, ecology, and biodiversity monitoring；
Disaster response, sustainability, and environmental policy support；
Control, robotics & autonomous systems；
CPS and digital twins；
Fluid dynamics, aero/thermo/structural analysis, and manufacturing；
Communications & networks, smart infrastructure, and energy systems；
Automated theorem proving, program synthesis for scientific computing；
Scientific workflows, MLOps, and experiment tracking in computational labs；
Computational economics, epidemiology, demography, and policy modeling；
Science of science, research knowledge graphs, meta-research；
Ethics, safety, governance, and equitable access to scientific AI；
Reproducibility, data sharing, licensing, and artifact evaluation；