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Found 20 papers, 2 papers with code
Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization
We present a method for end-to-end learning of Koopman surrogate models for optimal performance in control.
Nonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy
Polymer particle size constitutes a crucial characteristic of product quality in polymerization.
Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
We test the predictive quality of the model for following scenarios: i) when CMC data for surfactants are present in the training of the model in at least one different temperature, and ii) CMC data for surfactants are not present in the training, i. e., generalizing to unseen surfactants.
Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
We use Koopman theory for data-driven model reduction of nonlinear dynamical systems with controls.
Graph Neural Networks for Surfactant Multi-Property Prediction
A key factor in the predictive ability of QSPR and GNN models is the data available for training.
Data-Driven Model Reduction and Nonlinear Model Predictive Control of an Air Separation Unit by Applied Koopman Theory
Further, we present an NMPC implementation that uses derivative computation tailored to the fixed block structure of reduced Koopman models.
End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control
(Economic) nonlinear model predictive control ((e)NMPC) requires dynamic models that are sufficiently accurate and computationally tractable.
Gibbs-Duhem-Informed Neural Networks for Binary Activity Coefficient Prediction
In contrast to recent hybrid ML approaches, our approach does not rely on embedding a specific thermodynamic model inside the neural network and corresponding prediction limitations.
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms
Meta-learning of numerical algorithms for a given task consists of the data-driven identification and adaptation of an algorithmic structure and the associated hyperparameters.
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction
We recommend using sum pooling for the prediction of properties that depend on molecular size and compare pooling functions for properties that are molecular size-independent.
Graph neural networks for the prediction of molecular structure-property relationships
Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design.
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
We train the GNN on a database including more than 40, 000 AC values and compare it to a state-of-the-art MCM.
Graph Machine Learning for Design of High-Octane Fuels
We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space.
Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows
This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts.
Normalizing Flow-based Day-Ahead Wind Power Scenario Generation for Profitable and Reliable Delivery Commitments by Wind Farm Operators
We present a specialized scenario generation method that utilizes forecast information to generate scenarios for day-ahead scheduling problems.
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows
To model manifold data using normalizing flows, we employ isometric autoencoders to design embeddings with explicit inverses that do not distort the probability distribution.
Validation Methods for Energy Time Series Scenarios from Deep Generative Models
The design and operation of modern energy systems are heavily influenced by time-dependent and uncertain parameters, e. g., renewable electricity generation, load-demand, and electricity prices.
Principal Component Density Estimation for Scenario Generation Using Normalizing Flows
We train the resulting principal component flow (PCF) on data of PV and wind power generation as well as load demand in Germany in the years 2013 to 2015.
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty
Typically, several rival mechanistic models can explain the available data, so design of dynamic experiments for model discrimination helps optimally collect additional data by finding experimental settings that maximise model prediction divergence.
Global Optimization of Gaussian processes
Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in optimization problems.
