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AI for Science
Overview
Applying AI to scientific discovery, scientific modeling, and complex simulation, with emphasis on differentiable modeling, physics-informed learning, and robust inference.
Key Questions
- How can AI accelerate scientific discovery without sacrificing physical fidelity?
- What role can differentiable simulation play in end-to-end optimization of scientific models?
- How can multimodal AI systems handle complex scientific documents and data?
Our Contributions
- Developed multimodal AI systems for specialized scientific document analysis
- Explored AI methods for differentiable simulation and physics-informed learning
- Studied how scientific models can combine data, structure, and prior knowledge
Recommended Papers
Generates 4D scenes that stay physically consistent across time and materials.
Recent Work
Makes neural operators respect interface physics in multiphase flow simulation.
Embeds physics in graph networks for stable long-horizon multiphysics simulation.
Open Problems
- Bridging the gap between data-driven and physics-based models
- Ensuring interpretability and trustworthiness of AI in scientific applications
- Scaling differentiable simulation to complex real-world systems
Prospective Student Projects
- Differentiable simulation for engineering optimization
- Physics-informed neural networks for dynamic system modeling
- AI-assisted scientific literature mining and knowledge graph construction