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Sven Krippendorf (University of Cambridge)08/12/2025, 08:50
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Fabian Ruhle08/12/2025, 09:00
Kolmogorov-Arnold Networks exhibit several properties beneficial to scientific discovery. I will outline these properties and show how they can be leveraged to build more interpretable AI models, which I will illustrate with an example from representation theory. I will also discuss how the intrinsic structure of KANs facilitates symbolic regression by employing ideas similar to Google's...
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NABIL IQBAL08/12/2025, 09:45
One important organizing principle for building such neural networks is that of symmetry, i.e. the idea that the symmetries of the problem should be encoded in the architecture of the network. I will provide an introduction to the resulting field of “geometric deep learning”. I will then discuss our construction of a neural network that transforms nicely under the conformal group, i.e. the set...
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Prof. Gary Shiu (University of Wisconsin-Madison)08/12/2025, 11:00
Fine, regular, and star triangulations (FRSTs) of four-dimensional reflexive polytopes give rise to toric varieties, within which generic anticanonical hypersurfaces yield smooth Calabi-Yau threefolds. We introduce CYTransformer, a deep learning model based on the transformer architecture, to automate the generation of FRSTs. We demonstrate that CYTransformer efficiently and unbiasedly samples...
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Costis Papageorgakis (Queen Mary University of London)08/12/2025, 11:45
We introduce a neural network-based method to bootstrap crossing equations in Conformal Field Theory at finite temperature. Traditional approaches relying on positivity constraints or truncation schemes that discard infinite towers of operators are not applicable to this problem. Instead, we use MLPs to model spin-dependent tail functions that capture the combined contribution of infinitely...
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David Marsh (King's College London)08/12/2025, 13:30
The past few years have seen major advances in understanding the properties of axions in string theory. This progress is thanks to new computational tools that allow for fast and automated calculations with Calabi-Yau manifolds. I will describe the predictions string theory makes for axion masses, decay constants, and axion-photon couplings, and how these depend precisely on the topology of...
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Tancredi Schettini Gherardini (University of Bonn)08/12/2025, 14:00
A numerical scheme based on semi-supervised machine learning, "AInstein", was recently introduced (see https://iopscience.iop.org/article/10.1088/3050-287X/ae1117) to approximate generic Riemannian Einstein metrics on a given manifold. Its versatility stems from encoding the differentiable structure directly in the loss function, making the method applicable to manifolds constructed in a...
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Zhimei Liu (University of Cambridge)08/12/2025, 14:30
We address the inverse problem in Type IIB flux compactifications of identifying flux vacua with targeted phenomenological properties such as specific superpotential values or tadpole constraints using conditional generative models. These machine learning techniques overcome computational bottlenecks in traditional approaches such as rejection sampling and Markov Chain Monte Carlo (MCMC),...
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Yong Sheng Koay08/12/2025, 15:30
Transformers excel at natural language processing, raising the question of whether they can also learn the mathematical structures underlying particle physics. We introduce BART-L, a transformer-based model trained to generate particle physics Lagrangians from field content and symmetry information. Trained on Lagrangians consistent with the Standard Model gauge group SU(3)×SU(2)×U(1), BART-L...
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Yidi Qi (Northeastern University)08/12/2025, 16:00
Special Lagrangian (sLags) submanifolds are crucial objects for string phenomenology and the SYZ conjecture, yet their explicit construction remains a significant challenge in geometry. In this talk, we introduce a novel computational framework to tackle this problem. Our approach leverages a Quality-Diversity (QD) search algorithm to navigate families of parametrized geometries. This method...
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Luca Nutricati08/12/2025, 16:30
In this talk, we explore the phenomenological potential of heterotic line bundle models as a promising framework for deriving the Standard Model from string theory. We present a systematic approach to constrain the low-energy effective theories derived from such compactifications using remnants of anomalous U(1) symmetries to retrieve realistic quarks and leptons masses, mixing patterns and...
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Joseph Tooby-Smith09/12/2025, 09:00
This talk will discuss an open-source, community run, project called PhysLean, which aims to write
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physics into the interactive theorem prover Lean 4. That is, a computer programming language where
mathematical correctness is guaranteed. There are numerous benefits to this endeavor including
correctness, searchability of results, and the ability to interact with AI in a way safe from... -
Andre Lukas (U)09/12/2025, 09:45
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Magdalena Larfors (Uppsala university)09/12/2025, 11:00
We discus the application of two types of generative models are applied to the construction of ISD flux vacua of type IIB string theory. The models under study are Bayesian Flow Networks (BFNs) and Transformers. We find that both models demonstrate good performance, in particular on interpolation and conditional sampling. Some extrapolation capabilities are observed, which could be leveraged...
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Marc Truter09/12/2025, 11:45
In this talk, we show how deep reinforcement learning can be used to overcome the computational challenges faced by conventional search algorithms in the discovery of new Fano hypersurfaces.
By the Minimal Model Program, varieties can be reduced to three building blocks: Fano, Calabi-Yau, and general type varieties. The building blocks have singular points, which have a property known as...
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09/12/2025, 14:00
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James Halverson09/12/2025, 14:05
Machine learning (ML) systems fueled by neural networks have entered our daily lives and led to scientific breakthroughs, but many open questions remain. After a nod toward the question of rigor with ML and recent progress, I'll turn to the theory of neural networks. I will argue that understanding neural networks inevitably leads to ideas from quantum field theory, the theoretical framework...
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09/12/2025, 15:40
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Dr Andreas Schachner (Cornell University)10/12/2025, 09:00
String theory naturally gives rise to vast, high-dimensional datasets, yet systematic investigations of this landscape has long been impeded by the complexity of moduli spaces, quantum corrections, and the vastness of flux configurations. In this talk, I present new differentiable frameworks for string compactifications that combine automatic differentiation, and ML-based inference to...
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Lara San Martin Suarez (Caltech)10/12/2025, 09:45
The Z-hat invariant defined by Gukov–Pei–Putrov–Vafa is a BPS-counting q-series built from Calabi–Yau geometry, whose values at roots of unity recover the Witten–Reshetikhin–Turaev invariants of 3-manifolds. In this talk, I will review a construction for its knot-theoretic counterpart, the Gukov–Manolescu series F_K, and present the first large-scale computation of F_K for 1,246 knots,...
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Miranda Cheng10/12/2025, 11:00
In this talk, I present new results on (machine) learning of physics and mathematics. First, we report on advancements in training flow-based models to simulate quantum field theories (QFT). We advocate adopting physics-inspired generation paths and exploiting the physics information gained by learning such flows. The latter can be applied to simulate continuous families of theories in one go,...
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Evgeny Sobko10/12/2025, 11:45
Integrable systems are exactly solvable models that play a central role in QFT, string theory and statistical physics offering an ideal setting for understanding complex physical phenomena and developing novel analytical methods. However, the discovery of new integrable systems remains a major open challenge due to the nonlinearity of the Yang–Baxter equation (YBE) that defines them, and the...
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James Halverson10/12/2025, 13:30
I'll review the essentials of a neural network approach to defining and studying field theories, listing previous results related to symmetries, conformal symmetry, locality, interactions, etc. The focus of the talk will be on two 2025 papers, one related to QM, and the other related to fermions and SUSY. The QM results include a universality theorem, a new construction of Euclidean QM...
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Andrei Constantin10/12/2025, 14:15
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Vishnu Jejjala10/12/2025, 15:30
From holography, we know that gravitational systems have codimension one degrees of freedom. Through a number of experiments, we use machine learning to study physical and mathematical aspects of black hole entropy.
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Sven Krippendorf (University of Cambridge)10/12/2025, 16:15
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Costis Papageorgakis (Queen Mary University of London)
I will present a new bootstrap method for conformal field theories at finite temperature that uses neural networks to capture infinite operator contributions without positivity constraints or truncations. The approach combines the KMS condition, thermal dispersion relations, and neural networks that learn the high-spin behaviour of the thermal block expansion. I will demonstrate the method on...
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Gakuto Ogiwara (Saitama Institute of Technology)
We describe two related developments at the intersection of string theory, holography, and scientific machine learning. First, we develop a flexible physics-informed neural network (PINN) framework to solve boundary value problems for minimal surfaces in curved spacetimes, with a particular emphasis on handling singular geometries and moving boundaries. The method encodes the governing...
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