31 citations · 37 across the 3 of their papers we have counts for
7 papers
Deep reinforcement learning for process design: Review and perspective
Qinghe Gao, Artur M. Schweidtmann
The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches. Recently, breakthroughs in artificial i…
SFILES 2.0: An extended text-based flowsheet representation
Gabriel Vogel, Edwin Hirtreiter, Lukas Schulze Balhorn +1
SFILES are a text-based notation for chemical process flowsheets. They were originally proposed by d'Anterroches (Process flow sheet generation & design through a group contributio…
ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection
Giacomo Lastrucci, Artur M. Schweidtmann
Ensuring neural networks adhere to domain-specific constraints is crucial for addressing safety and trustworthiness while also enhancing inference accuracy. Despite the nonlinear n…
Text2Model: Generating dynamic chemical reactor models using large language models (LLMs)
Sophia Rupprecht, Yassine Hounat, Monisha Kumar +2
As large language models have shown remarkable capabilities in conversing via natural language, the question arises as to how LLMs could potentially assist chemical engineers in re…
Deterministic Global Optimization over trained Kolmogorov Arnold Networks
Tanuj Karia, Giacomo Lastrucci, Artur M. Schweidtmann
To address the challenge of tractability for optimizing mathematical models in science and engineering, surrogate models are often employed. Recently, a new class of machine learni…
Transferring Graph Neural Networks for Soft Sensor Modeling using Process Topologies
Maximilian F. Theisen, Gabrie M. H. Meesters, Artur M. Schweidtmann
Data-driven soft sensors help in process operations by providing real-time estimates of otherwise hard- to-measure process quantities, e.g., viscosities or product concentrations.…