Data augmentation for machine learning of chemical process flowsheets
Fuente:
arXiv
Guardado en:
| Autores principales: | Balhorn, Lukas Schulze, Hirtreiter, Edwin, Luderer, Lynn, Schweidtmann, Artur M. |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence
por: Hirtreiter, Edwin, et al.
Publicado: (2022)
por: Hirtreiter, Edwin, et al.
Publicado: (2022)
SFILES 2.0: An extended text-based flowsheet representation
por: Vogel, Gabriel, et al.
Publicado: (2022)
por: Vogel, Gabriel, et al.
Publicado: (2022)
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
por: Vogel, Gabriel, et al.
Publicado: (2022)
por: Vogel, Gabriel, et al.
Publicado: (2022)
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
por: Stops, Laura, et al.
Publicado: (2022)
por: Stops, Laura, et al.
Publicado: (2022)
Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence
por: Balhorn, Lukas Schulze, et al.
Publicado: (2024)
por: Balhorn, Lukas Schulze, et al.
Publicado: (2024)
Graph neural networks for the prediction of molecular structure-property relationships
por: Rittig, Jan G., et al.
Publicado: (2022)
por: Rittig, Jan G., et al.
Publicado: (2022)
Toward generalizable learning of all (linear) first-order methods via memory augmented Transformers
por: Dutta, Sanchayan, et al.
Publicado: (2024)
por: Dutta, Sanchayan, et al.
Publicado: (2024)
The impact of machine learning forecasting on strategic decision-making for Bike Sharing Systems
por: Angelelli, Enrico, et al.
Publicado: (2026)
por: Angelelli, Enrico, et al.
Publicado: (2026)
Common pitfalls to avoid while using multiobjective optimization in machine learning
por: Akhter, Junaid, et al.
Publicado: (2024)
por: Akhter, Junaid, et al.
Publicado: (2024)
Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction
por: Horenko, Illia, et al.
Publicado: (2023)
por: Horenko, Illia, et al.
Publicado: (2023)
Deterministic Global Optimization over trained Kolmogorov Arnold Networks
por: Karia, Tanuj, et al.
Publicado: (2025)
por: Karia, Tanuj, et al.
Publicado: (2025)
Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning
por: Chen, Paula, et al.
Publicado: (2023)
por: Chen, Paula, et al.
Publicado: (2023)
Early predicting of hospital admission using machine learning algorithms: Priority queues approach
por: Antczak, Jakub, et al.
Publicado: (2026)
por: Antczak, Jakub, et al.
Publicado: (2026)
Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
por: Langdon, Becky, et al.
Publicado: (2026)
por: Langdon, Becky, et al.
Publicado: (2026)
Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
por: Schulze, Jan C., et al.
Publicado: (2024)
por: Schulze, Jan C., et al.
Publicado: (2024)
The inexact power augmented Lagrangian method for constrained nonconvex optimization
por: Bodard, Alexander, et al.
Publicado: (2024)
por: Bodard, Alexander, et al.
Publicado: (2024)
Topology optimization of periodic lattice structures for specified mechanical properties using machine learning considering member connectivity
por: Matsuoka, Tomoya, et al.
Publicado: (2024)
por: Matsuoka, Tomoya, et al.
Publicado: (2024)
Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems
por: Hu, Qifeng, et al.
Publicado: (2025)
por: Hu, Qifeng, et al.
Publicado: (2025)
A Minimax-MDP Framework with Future-imposed Conditions for Learning-augmented Problems
por: Chen, Xin, et al.
Publicado: (2025)
por: Chen, Xin, et al.
Publicado: (2025)
When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach
por: Chen, Qian, et al.
Publicado: (2025)
por: Chen, Qian, et al.
Publicado: (2025)
Revealing design archetypes and flexibility in e-molecule import pathways using Modeling to Generate Alternatives and interpretable machine learning
por: Kchaou, Mahdi, et al.
Publicado: (2025)
por: Kchaou, Mahdi, et al.
Publicado: (2025)
Approximate non-linear model predictive control with safety-augmented neural networks
por: Hose, Henrik, et al.
Publicado: (2023)
por: Hose, Henrik, et al.
Publicado: (2023)
Iteratively reweighted kernel machines efficiently learn sparse functions
por: Zhu, Libin, et al.
Publicado: (2025)
por: Zhu, Libin, et al.
Publicado: (2025)
A Schrödinger Eigenfunction Method for Long-Horizon Stochastic Optimal Control
por: Claeys, Louis, et al.
Publicado: (2026)
por: Claeys, Louis, et al.
Publicado: (2026)
Data-driven robust Markov decision processes on Borel spaces: performance guarantees via an axiomatic approach
por: Ramani, Sivaramakrishnan
Publicado: (2026)
por: Ramani, Sivaramakrishnan
Publicado: (2026)
Enhancing supply chain security with automated machine learning
por: Wang, Haibo, et al.
Publicado: (2024)
por: Wang, Haibo, et al.
Publicado: (2024)
Global Optimization of Gaussian processes
por: Schweidtmann, Artur M., et al.
Publicado: (2020)
por: Schweidtmann, Artur M., et al.
Publicado: (2020)
PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning
por: Jentzen, Arnulf, et al.
Publicado: (2025)
por: Jentzen, Arnulf, et al.
Publicado: (2025)
Restarted contractive operators to learn at equilibrium
por: Davy, Leo, et al.
Publicado: (2025)
por: Davy, Leo, et al.
Publicado: (2025)
Quantum Boltzmann machine learning of ground-state energies
por: Patel, Dhrumil, et al.
Publicado: (2024)
por: Patel, Dhrumil, et al.
Publicado: (2024)
The augmented NLP bound for maximum-entropy remote sampling
por: Ponte, Gabriel, et al.
Publicado: (2026)
por: Ponte, Gabriel, et al.
Publicado: (2026)
MathOptAI.jl: Embed trained machine learning predictors into JuMP models
por: Dowson, Oscar, et al.
Publicado: (2025)
por: Dowson, Oscar, et al.
Publicado: (2025)
Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness
por: Bouška, Michal, et al.
Publicado: (2024)
por: Bouška, Michal, et al.
Publicado: (2024)
Self-Supervised Learning of Iterative Solvers for Constrained Optimization
por: Lüken, Lukas, et al.
Publicado: (2024)
por: Lüken, Lukas, et al.
Publicado: (2024)
On characterizing optimal learning trajectories in a class of learning problems
por: Befekadu, Getachew K
Publicado: (2025)
por: Befekadu, Getachew K
Publicado: (2025)
Projection-Free Functional Constrained Optimization for Risk Aversion and Sparsity Control
por: Cheng, Yi, et al.
Publicado: (2022)
por: Cheng, Yi, et al.
Publicado: (2022)
Wasserstein Distributionally Robust Regret Optimization
por: Fiechtner, Lukas-Benedikt, et al.
Publicado: (2025)
por: Fiechtner, Lukas-Benedikt, et al.
Publicado: (2025)
Pruning for efficient deterministic global optimization over trained ReLU neural networks
por: Lastrucci, Giacomo, et al.
Publicado: (2026)
por: Lastrucci, Giacomo, et al.
Publicado: (2026)
Distributional Adversarial Attacks and Training in Deep Hedging
por: He, Guangyi, et al.
Publicado: (2025)
por: He, Guangyi, et al.
Publicado: (2025)
Hybrid physics-informed metabolic cybergenetics: process rates augmented with machine-learning surrogates informed by flux balance analysis
por: Espinel-Ríos, Sebastián, et al.
Publicado: (2024)
por: Espinel-Ríos, Sebastián, et al.
Publicado: (2024)
Ejemplares similares
-
Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence
por: Hirtreiter, Edwin, et al.
Publicado: (2022) -
SFILES 2.0: An extended text-based flowsheet representation
por: Vogel, Gabriel, et al.
Publicado: (2022) -
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
por: Vogel, Gabriel, et al.
Publicado: (2022) -
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
por: Stops, Laura, et al.
Publicado: (2022) -
Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence
por: Balhorn, Lukas Schulze, et al.
Publicado: (2024)