paper-with-me

홈 › Papers

AI Insights into Theoretical Physics and the Swampland Program: A Journey Through the Cosmos with ChatGPT

2023-01-10 · Kay Lehnert

In this case study, we explore the capabilities and limitations of ChatGPT, a natural language processing model developed by OpenAI, in the field of string theoretical swampland conjectures. We find that it is effective at paraphrasing and explaining concepts in a variety of styles, but not at genuinely connecting concepts. It will provide false information with full confidence and make up statements when necessary. However, its ingenious use of language can be fruitful for identifying analogies and describing visual representations of abstract concepts.

📄 PDF Abstract BibTeX arXiv:2301.08155

Code (1)

kabeleh/chatgpt 공식 구현

Similar Papers 제목 키워드 기반

Machine Learning the 6d Supergravity Landscape

2025-05-22 · Nathan Brady, David Tennyson, Thomas Vandermeulen

In this paper, we apply both supervised and unsupervised machine learning algorithms to the study of the string landscape and swampland in 6-dimensions. Our data are the (almost) anomaly-free 6-dimensional $\mathcal{N} =…

Lyapunov weights to convey the meaning of time in physics-informed neural networks

2024-07-31

Time is not a dimension as the others. In Physics-Informed Neural Networks (PINN) several proposals attempted to adapt the time sampling or time weighting to take into account the specifics of this special dimension. But…

Integer linear programming for unsupervised training set selection in molecular machine learning

2024-10-21 · Matthieu Haeberle, Puck van Gerwen, Ruben Laplaza, Ksenia R. Briling 외

Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning applied to chem…

Modelling contextuality by probabilistic programs with hypergraph semantics

2018-01-31 · Peter D. Bruza

Models of a phenomenon are often developed by examining it under different experimental conditions, or measurement contexts. The resultant probabilistic models assume that the underlying random variables, which define a …

Probabilistic Programming

End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics

2023-05-17 · NeurIPS 2023 11

High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before measurements can be compared to certain theo…