🤖 AI Summary
Parameter identification remains a critical bottleneck for predictive modeling and control using mechanistic ordinary differential equation (ODE) models, hindered by noisy data, model misspecification, implementation complexity, and stringent differentiability requirements. This paper introduces an intelligent AI workflow tailored for ODE systems: it automatically compiles high-performance, differentiable JAX functions from XML-based model specifications and Python skeleton code; proposes a proxy-based AI architecture enabling model-code consistency verification and automated error correction; and integrates a two-stage parameter estimation strategy combining global search with gradient-based optimization. The framework substantially lowers the barrier to mechanistic modeling, delivering an end-to-end, reproducible, and auditable parameter estimation pipeline. An open-source implementation significantly reduces manual coding and debugging effort while ensuring numerical robustness and computational efficiency.
📝 Abstract
Parameter identification for mechanistic Ordinary Differential Equation (ODE) models underpins prediction and control in several applications, yet remains a labor-intensive and brittle process: datasets are noisy and partial, models can be stiff or misspecified, and differentiable implementations demand framework expertise. An agentic AI workflow is presented that converts a lightweight, human-readable specification into a compiled, parallel, and differentiable calibration pipeline. Users supply an XML description of the problem and fill in a Python code skeleton; the agent automatically validates consistency between spec and code, and auto-remediates common pathologies. It transforms Python callables into pure JAX functions for efficient just-in-time compilation and parallelization. The system then orchestrates a two-stage search comprising global exploration of the parameter space followed by gradient-based refinement. The result is an AD-native, reproducible workflow that lowers the barrier to advanced calibration while preserving expert control. An open-source implementation with a documented API and examples is released, enabling rapid movement from problem statement to fitted, auditable models with minimal boilerplate.