On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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10 results for “Biomanufacturing”

Sensitivity Analysis on Policy-Augmented Graphical Hybrid Models with Shapley Value Estimationstat.ML

Framework for sensitivity analysis in biomanufacturing processes.

problem High complexity and uncertainty in biomanufacturing processes.

method Shapley value estimation for linear and nonlinear pKG models, using quasi-Monte Carlo and antithetic sampling.

result Improved efficiency and accuracy in sensitivity analysis for biomanufacturing processes.

biomanufacturing sensitivity analysis Shapley value linear Gaussian models quasi-Monte Carlo

2024-11-20abs ↗pdf ↗

Digital Twin Calibration for Biological System-of-Systems: Cell Culture Manufacturing Processq-bio.QM

Optimizes biomanufacturing processes with a new digital twin calibration method.

problem Lack of interpretability and sample efficiency in traditional DoE methods.

method Developed a computational approach to calibrate Bio-SoS digital twin model.

result Guides sample-efficient and interpretable DoEs by quantifying sub-model parameter estimation errors.

Biomanufacturing Digital Twin Biological System-of-Systems Design of Experiments Parameter Estimation

2024-05-07abs ↗pdf ↗

Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and Controlcs.LG

A green simulation-assisted reinforcement learning method for biomanufacturing.

problem Complexity, high variability, lead time, and limited historical data in biopharmaceutical manufacturing.

method Quantifies model risk, uses posterior distribution, and selectively reuses simulation data.

result Demonstrates promising performance in online learning and decision making.

reinforcement learning biomanufacturing model risk green simulation process learning

2020-06-17abs ↗pdf ↗

RAPTOR-GEN: RApid PosTeriOR GENerator for Bayesian Learning in Biomanufacturingstat.ML

RAPTOR-GEN accelerates digital twin development for biopharmaceuticals.

problem Lack of agility in rapid, on-demand production of biotherapeutics due to bioprocess complexity.

method Bayesian learning framework based on a multi-scale probabilistic knowledge graph (pKG) and stochastic differential equations (SDE).

result RAPTOR-GEN accelerates intelligent digital twin development from sparse and heterogeneous experimental data.

Bayesian learning digital twin biopharmaceuticals biomanufacturing sparse data

2025-09-25abs ↗pdf ↗

Linear Noise Approximation Assisted Bayesian Inference on Mechanistic Model of Partially Observed Stochastic Reaction Networkstat.ML

Paper develops efficient Bayesian inference for enzymatic SRNs with LNA metamodel.

problem Bayesian inference for nonlinear SDE-based mechanistic models with partial observations and measurement errors.

method Interpretable Bayesian updating LNA metamodel and efficient posterior sampling.

result Proposed approach demonstrates promising performance in empirical studies.

Bayesian inference LNA metamodel Stochastic reaction network Mechanistic model Digital twin

2024-05-05abs ↗pdf ↗

Adjoint Sensitivity Analysis on Multi-Scale Bioprocess Stochastic Reaction Networkq-bio.MN

Adjoint SA speeds up bioprocess parameter learning.

problem Challenges in digital twin development for biomanufacturing.

method Adjoint sensitivity analysis on multi-scale enzymatic reaction networks.

result Resilient sensitivities reveal bioprocess regulatory mechanisms.

adjoint sensitivity biomanufacturing stochastic reaction networks parameter learning digital twin

2024-05-07abs ↗pdf ↗

Interpretable Biomanufacturing Process Risk and Sensitivity Analyses for Quality-by-Design and Stability Controlstat.ML

While biomanufacturing plays a significant role in supporting the economy and ensuring public health, it faces critical challenges, including complexity, high variability, lengthy lead time, and very limited process data, especially for personalized new cell and gene biotherapeutics. Driven by these challenges, we prop…

2019-09-10abs ↗pdf ↗

Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Controlstat.ML

A new method uses ABC-SMC to infer hybrid models in bioprocesses with limited data.

problem Inference of hybrid models in bioprocesses with limited real data and high uncertainties.

method Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) and linear Gaussian dynamic Bayesian network (LG-DBN) for posterior distribution approximation.

result The method accelerates hybrid model inference and supports process monitoring and robust control.

bioprocess Bayesian inference ABC-SMC LG-DBN dynamic Bayesian network

2022-05-05abs ↗pdf ↗

A Symbolic and Statistical Learning Framework to Discover Bioprocessing Regulatory Mechanism: Cell Culture Examplestat.ML

Paper develops a framework to discover bioprocessing regulatory mechanisms using symbolic and statistical learning.

problem Challenges in modeling complex intracellular regulation, stochastic system behavior, and limited experimental data.

method Symbolic and statistical learning framework based on stochastic differential equations and Bayesian learning.

result Improved sample efficiency and robust model selection compared to state-of-the-art approaches.

Bioprocessing Regulatory Mechanisms Bayesian Learning Symbolic Learning Metropolis-adjusted Langevin Algorithm

2025-05-06abs ↗pdf ↗

Biomanufacturing Harvest Optimization with Small Datastat.ML

Optimizes harvesting in biopharmaceutical fermentation with limited data.

problem High variability in fermentation outputs due to model risk with small data.

method Stochastic model, Bayesian approach, Markov decision process.

result Improves fermentation output and reduces variability.

fermentation biopharmaceutical model risk stochastic modeling Markov decision process

2021-01-11abs ↗pdf ↗