62 citations · 177 across the 11 of their papers we have counts for
11 papers
Pruning for efficient deterministic global optimization over trained ReLU neural networks
Giacomo Lastrucci, Tanuj Karia, Victor Schulte +2
Neural networks are increasingly used as surrogates in optimization problems to replace computationally expensive models. However, embedding ReLU neural networks in mathematical pr…
GraphRAG for Engineering Diagrams: ChatP&ID Enables LLM Interaction with P&IDs
Achmad Anggawirya Alimin, Artur M. Schweidtmann
Large Language Models (LLMs) combined with Retrieval-Augmented Generation (RAG) and knowledge graphs offer new opportunities for interacting with engineering diagrams such as Pipin…
Grounding Large Language Models in Reaction Knowledge Graphs for Synthesis Retrieval
Olga Bunkova, Lorenzo Di Fruscia, Sophia Rupprecht +3
Large Language Models (LLMs) can aid synthesis planning in chemistry, but standard prompting methods often yield hallucinated or outdated suggestions. We study LLM interactions wit…
Multi-agent systems for chemical engineering: A review and perspective
Sophia Rupprecht, Qinghe Gao, Tanuj Karia +1
Large language model (LLM)-based multi-agent systems (MASs) are a recent but rapidly evolving technology with the potential to transform chemical engineering by decomposing complex…
Talking like Piping and Instrumentation Diagrams (P&IDs)
Achmad Anggawirya Alimin, Dominik P. Goldstein, Lukas Schulze Balhorn +1
We propose a methodology that allows communication with Piping and Instrumentation Diagrams (P&IDs) using natural language. In particular, we represent P&IDs through the DEXPI data…
Rule-based autocorrection of Piping and Instrumentation Diagrams (P&IDs) on graphs
Lukas Schulze Balhorn, Niels Seijsener, Kevin Dao +4
A piping and instrumentation diagram (P&ID) is a central reference document in chemical process engineering. Currently, chemical engineers manually review P&IDs through visual insp…
Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence
Lukas Schulze Balhorn, Kevin Degens, Artur M. Schweidtmann
Control structure design is an important but tedious step in P&ID development. Generative artificial intelligence (AI) promises to reduce P&ID development time by supporting engine…
Toward autocorrection of chemical process flowsheets using large language models
Lukas Schulze Balhorn, Marc Caballero, Artur M. Schweidtmann
The process engineering domain widely uses Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (P&IDs) to represent process flows and equipment configurations. Ho…
Empirical assessment of ChatGPT's answering capabilities in natural science and engineering
Lukas Schulze Balhorn, Jana M. Weber, Stefan Buijsman +3
ChatGPT is a powerful language model from OpenAI that is arguably able to comprehend and generate text. ChatGPT is expected to greatly impact society, research, and education. An e…
Toward automatic generation of control structures for process flow diagrams with large language models
Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann
Developing Piping and Instrumentation Diagrams (P&IDs) is a crucial step during process development. We propose a data-driven method for the prediction of control structures. Our m…
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
Gabriel Vogel, Lukas Schulze Balhorn, Artur M. Schweidtmann
We propose a novel method enabling autocompletion of chemical flowsheets. This idea is inspired by the autocompletion of text. We represent flowsheets as strings using the text-bas…