Senior researchers working on inverse problems through two neuro-symbolic methods — consolidated SciML, and LLM-orchestrated RAG-mediated calibration of complex simulators.
The Institute reads the inverse problem as a precise operational act: a material to be inverted (heterogeneous evidence) is converted into a configuration accepted by a functional receiver (a structured model). The roles are not fixed — they materialise only at the moment of inversion.
Measurements and structural priors inverted into parameters and closures for ODE/PDE/state-space models, augmented by PINNs, UDEs, or graph neural networks.
A hybrid syntactic-vector RAG under LLM orchestration assembles the parametric profiles and boundary structures a mechanistic simulator accepts.