The paper presents a bias-aware digital‑twin calibration and control framework for multiscale bioprocess models, using stochastic differential equations and quasi‑likelihood estimation with adjoint sensitivity analysis. It introduces SDE generator‑based moment expansions to quantify truncation‑induced parameter bias and forward‑backward adjoints to trace how calibration uncertainty affects value functions and policy performance. An Actor‑Simulator algorithm is proposed to jointly update model parameters, select informative experiments, and optimize control policies, achieving improved calibration accuracy, sample efficiency, and control performance in numerical studies.
arXiv:2610.09505v2 Announce Type: replace Abstract: We develop a bias-aware digital-twin calibration and control framework for multiscale bioprocess models within a biological systems-of-systems (Bio...
arXiv:2607. 18164v1 Announce Type: cross Abstract: Digital Twins rely on surrogate models to mirror physical systems in real time, yet these models can degrade as operating conditions evolve, a phenomenon known as concept drift.
The paper presents a hybrid edge‑cloud digital twin for welfare‑constrained environmental control in poultry farms. It combines distributed sensing, on‑device state estimation, a physics‑based thermodynamic model with a learned residual, and model predictive control to adaptively manage temperature and ammonia levels. Experiments in a broiler testbed show significant improvements: temperature prediction error drops from 1.8 °C to 0.4 °C, ammonia violations fall by 90 %, and communication needs are reduced 30‑fold, with a Domain Transfer Score of 0.92 indicating strong cross‑facility robustness.
arXiv:2508.10970v2 Announce Type: replace-cross Abstract: Bioprocesses are central to modern biotechnology, enabling sustainable production of pharmaceuticals, specialty chemicals, cosmetics, and foo...
arXiv:2606. 19230v1 Announce Type: new Abstract: This work presents an extension to Pareto Front Guided Sampling (PFGS), a Human-in-the-Loop (HitL) Bayesian Optimization (BO) framework in which Gaussian process (GP) surrogate-derived quantities are reformulated as objectives of a multi-objective optimization problem, and the resulting Pareto front is exposed to a domain expert for interactive candidate selection rather than returning a single automated recommendation.
arXiv:2606. 16219v1 Announce Type: cross Abstract: Digital twin modeling, including control and data assimilation under model uncertainty, often faces an open-ended fidelity problem: adding variables, data streams, and time scales can indefinitely increase model complexity, ultimately producing systems that are difficult to maintain, validate, interpret, and use for stress or safety testing.
This work presents an extension to Pareto Front Guided Sampling (PFGS), a Human-in-the-Loop (HitL) Bayesian Optimization (BO) framework in which Gaussian process (GP) surrogate-derived quantities are reformulated as objectives of a multi-objective optimization problem, and the resulting Pareto front is exposed to a domain expert for interactive candidate selection rather than returning a single automated recommendation. The framework is extended in two directions: constrained optimization is addressed by incorporating the posterior probability of satisfying output specification limits as an explicit Pareto objective, computed analytically from the GP posterior distribution; robust optimization is addressed by a Monte Carlo sampling strategy that estimates expected lower-confidence performance over a user-defined variability of input perturbations, capturing performance degradation under likely implementation deviations.
arXiv:2608. 08631v1 Announce Type: new Abstract: Digital-twin calibration requires interaction data that is expensive to collect.
arXiv:2610.27563v1 Announce Type: cross Abstract: Stochastic optimization with high-fidelity nonlinear models is often computationally prohibitive because each objective evaluation requires sampling-...
arXiv:2607. 05758v1 Announce Type: cross Abstract: Automated experimentation is moving from closed-loop optimization toward open decision-making, where human or AI planners must forecast the consequences of candidate actions before executing them.
Optimizing industrial process flowsheets is often computationally prohibitive due to the high cost of rigorous simulations and the curse of dimensionality inherent in complex design spaces. To address...
NeuronSifter is a decision‑centric Agent‑in‑Twin framework that uses an evolving biological digital twin instead of a fixed predictive model to prioritize central nervous system interventions. It compiles interventions into state‑conditional target‑occupancy representations, propagates them through a mechanism‑grounded World Action Model, and selects informative measurements to reduce decision uncertainty. The system is evaluated on Alzheimer’s disease scenarios, demonstrating adaptive evidence acquisition, uncertainty‑aware planning, and robustness to model discrepancy, while continuously refining its decision‑relevant representations with externally verified evidence.