Lin Du.

The agent proposes bounded design parameters. A Python controller checks the schema, geometry and feasibility before a fixed Java/COMSOL implementation constructs and solves the model.

Each attempt retains its proposal, validation outcome, solver log, raw exports and derived metrics. Isolated solver processes and explicit execution states make failures inspectable and iterations reproducible.

Outcome: The selected design increased reservoir area from 46.18 to 49.38 mm² while reducing surface t90 from 32.01 to 29.34 minutes. This remains a simulation-based proof of principle; physical validation and autonomous laboratory execution are future work.

Measurement → representation: peak detection, baseline subtraction and Gaussian/GMM feature extraction convert cyclic voltammograms into structured model inputs. A companion simulator learns concentration-to-peak relationships to generate synthetic training data.

Representation → prediction: Optimized IdentQuantNet combines identification with drug-specific, multi-branch quantification. Interaction-aware modelling and tailored asymmetric losses account for interference and unequal error costs.

Validation: simulated and independently measured datasets, with ETO, MTX, IFO, CP and 5-FU across the studied single-agent and interacting-drug cases. Read the signal-simulation paper ↗

ETODA integrates population pharmacokinetic models, patient covariates, Bayesian posterior estimation, Monte Carlo simulations and candidate-regimen scoring. The result is a three-dimensional error-tolerance grid across true concentration, measured concentration and sampling time.

The published study evaluates imatinib and vancomycin using 50 × 50 concentration grids. It exposes drug-specific sensitivity to measurement errors and the effect of when a sample is collected.

This is a computational evaluation layer for model-informed precision dosing. The grid shown here explains the concept; it is not a reproduction of the published simulation results.