Cover Image

Data-Driven Conditional Flexibility Index

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wedemeyer, Moritz, Cramer, Eike, Mitsos, Alexander, Dahmen, Manuel
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Robust Energy System Design via Semi-infinite Programming
by: Wedemeyer, Moritz, et al.
Published: (2024)

Hybrid Neural Ordinary Differential Equations for Data-Efficient Polymerization Modeling with Incomplete Kinetics
by: Almanasreh, Marah, et al.
Published: (2026)

Robust Design of Multi-Energy Systems Accounting for Mixed-Integer Operational Problems
by: Wedemeyer, Moritz, et al.
Published: (2026)

Iterative Model-Learning Scheme via Gaussian Processes for Nonlinear Model Predictive Control of (Semi-)Batch Processes
by: Tan, Tai Xuan, et al.
Published: (2026)

End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control
by: Mayfrank, Daniel, et al.
Published: (2023)

Sample-Efficient Reinforcement Learning of Koopman eNMPC
by: Mayfrank, Daniel, et al.
Published: (2025)

Bayesian Optimization of Partially Known Systems using Hybrid Models
by: Cramer, Eike, et al.
Published: (2026)

Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data
by: Velioglu, Mehmet, et al.
Published: (2024)

Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization
by: Mayfrank, Daniel, et al.
Published: (2024)

End-to-End Reinforcement Learning of Koopman Models for eNMPC of an Air Separation Unit
by: Mayfrank, Daniel, et al.
Published: (2025)

Least Squares and Marginal Log-Likelihood Model Predictive Control using Normalizing Flows
by: Cramer, Eike
Published: (2024)

Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows
by: Hilger, Hannes, et al.
Published: (2023)

Estimating Dense-Packed Zone Height in Liquid-Liquid Separation: A Physics-Informed Neural Network Approach
by: Velioglu, Mehmet, et al.
Published: (2026)

Molecular Machine Learning in Chemical Process Design
by: Rittig, Jan G., et al.
Published: (2025)

DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction tasks
by: Pavšek, Jan, et al.
Published: (2025)

Graph neural networks for the prediction of molecular structure-property relationships
by: Rittig, Jan G., et al.
Published: (2022)

Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
by: Rittig, Jan G., et al.
Published: (2022)

Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction
by: Schweidtmann, Artur M., et al.
Published: (2022)

Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
by: Schulze, Jan C., et al.
Published: (2024)

Graph Machine Learning for Design of High-Octane Fuels
by: Rittig, Jan G., et al.
Published: (2022)

Field theory for optimal signal propagation in ResNets
by: Fischer, Kirsten, et al.
Published: (2023)

Thermodynamics-Consistent Graph Neural Networks
by: Rittig, Jan G., et al.
Published: (2024)

Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison
by: Schulze, Jan C., et al.
Published: (2025)

Differentiable Thermodynamic Phase-Equilibria for Machine Learning
by: Hicham, Karim K. Ben, et al.
Published: (2026)

Clapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
by: Pavšek, Jan, et al.
Published: (2026)

Tabular foundation models for in-context prediction of molecular properties
by: Hicham, Karim K. Ben, et al.
Published: (2026)

Paired Wasserstein Autoencoders for Conditional Sampling
by: Piening, Moritz, et al.
Published: (2024)

Variationally correct operator learning: Reduced basis neural operator with a posteriori error estimation
by: Qiu, Yuan, et al.
Published: (2025)

DPG loss functions for learning parameter-to-solution maps by neural networks
by: Castillo, Pablo Cortés, et al.
Published: (2025)

Graph Neural Networks for Surfactant Multi-Property Prediction
by: Brozos, Christoforos, et al.
Published: (2024)

Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
by: Brozos, Christoforos, et al.
Published: (2024)

Predicting the Temperature-Dependent CMC of Surfactant Mixtures with Graph Neural Networks
by: Brozos, Christoforos, et al.
Published: (2024)

Tractable Transformers for Flexible Conditional Generation
by: Liu, Anji, et al.
Published: (2025)

Goal-Conditioned Reinforcement Learning for Data-Driven Maritime Navigation
by: Vaidheeswaran, Vaishnav, et al.
Published: (2025)

Sensitivity, Specificity, and Consistency: A Tripartite Evaluation of Privacy Filters for Synthetic Data Generation
by: Koeken, Adil, et al.
Published: (2025)

Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?
by: Georgiou, Anastasia, et al.
Published: (2025)

CHEQ-ing the Box: Safe Variable Impedance Learning for Robotic Polishing
by: Cramer, Emma, et al.
Published: (2025)

Nonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy
by: Koronaki, Eleni D., et al.
Published: (2024)

Empowering Aggregators with Practical Data-Driven Tools: Harnessing Aggregated and Disaggregated Flexibility for Demand Response
by: Mylonas, Costas, et al.
Published: (2024)

Data-Driven Soil Organic Carbon Sampling: Integrating Spectral Clustering with Conditioned Latin Hypercube Optimization
by: Zhao, Weiying, et al.
Published: (2025)