DEEP-INセミナー
17 イベント
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セミナーGenerative Models for Statistical Field Theories
2025年6月25日(水) 15:00 - 16:00
王 凌霄 (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 研究員)
In the final talk of the DEEP-IN series, we will explore the role of generative models in learning phase transitions and sampling in lattice systems. First, we demonstrate how generative models can serve as global samplers by learning the underlying probability distributions. This enables the sampling of configurations more efficiently for lattice field theories. We will also demonstrate how the ferromagnetic phase transition, the Kosterlitz-Thouless transition, and quantum phase transitions can be identified from generative models. I will briefly introduce generative diffusion models, which can be interpreted as a stochastic quantization scheme. This opens a new path for understanding deep generative models. This is an informal seminar, we will start with the methodology and some practical examples, and finally reserve time for everyone interested to discuss it together.
会場: 理化学研究所 和光キャンパス 研究本館3階 345-347 (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナーGauge-equivariant multigrid neural networks
2025年6月19日(木) 10:30 - 12:00
Tilo Wettig (Professor, Universität Regensburg, Germany)
In lattice QCD simulations, the most time-consuming element is typically the solution of the Dirac equation in the presence of a given gauge field. The current state of the art is to use a multigrid preconditioner to reduce the condition number of the Dirac operator matrix. We show how such preconditioners can be constructed using gauge-equivariant neural networks. For the multigrid solve we employ parallel-transport convolution layers. For the multigrid setup we consider two versions: the standard construction based on the near-null space of the operator, and a gauge-equivariant construction using pooling and subsampling layers. We show that both versions eliminate critical slowing down.
会場: セミナー室 (359号室) 3階 359号室とZoomのハイブリッド開催
イベント公式言語: 英語
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セミナー
Identifying Lightning Structures and Predicting Cloud Properties
2025年6月18日(水) 15:00 - 16:00
王 凌霄 (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 研究員)
This third talk in the DEEP-IN series focuses on using unsupervised machine learning to identify and predict patterns in atmospheric phenomena. We begin by demonstrating how clustering and dimensionality reduction techniques can uncover coherent lightning patterns from high-dimensional LOFAR (LOw Frequency ARray) data, offering insight into large-scale organization. We then show how generative diffusion models enable super-resolution retrieval of cloud properties for all day from satellite observations. This is an informal seminar, we will start with the methodology and some practical examples, and finally reserve time for everyone interested to discuss it together.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Collective Behaviors and Deep Learning Applications
2025年6月11日(水) 15:00 - 16:00
王 凌霄 (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 研究員)
Understanding and modeling collective pedestrian behavior, particularly under extreme conditions, is a challenging problem that combines cognition, physics, and data analysis. In the second talk of DEEP-IN series, I will explore how deep learning can reveal the underlying principles of crowd dynamics from data. Starting with a bounded rationality framework, we demonstrate how deep learning can quantify evacuation dynamics and reveal hidden patterns in collective motion. Specifically, we demonstrate how macroscopic observables, such as entropy and kinetic energy, can be extracted from microscopic trajectories in simulations and real-world data. This is an informal seminar, we will start with the methodology and some practical examples, and finally reserve time for everyone interested to discuss it together.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Solving Inverse Problems with Physics-Driven Deep Learning
2025年6月4日(水) 15:00 - 16:00
王 凌霄 (理化学研究所 数理創造研究センター (iTHEMS) 数理基礎部門 研究員)
This talk kicks off a four-part seminar series on the DEEP-IN WG, an interdisciplinary working group exploring how modern deep learning — including deep generative models — can tackle inverse problems across scientific domains. In addition to DEEP-IN activities, I will present a new framework and vision, motivated by the growing synergy between physics-driven designs for deep learning and scientific discovery, as discussed in our recent review article. Future talks will demonstrate machine learning applications in collective behaviors, weather systems, and lattice field simulations. This is an informal seminar, we will start with the methodology, give some practical examples, and finally reserve time for everyone interested to discuss it together.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Can we infer probability distributions from cumulants? Probabilistic approaches to inverse problems
2025年3月18日(火) 15:30 - 16:30
Yang-Yang Tan (Ph.D. Candidate, Dalian University of Technology, China)
Inverse problems, which involve estimating system inputs from outputs, are prevalent across science and engineering. Their ill-posed nature often makes finding numerically stable and unique solutions challenging. This seminar explores probabilistic methods for reconstructing distributions from a finite set of their moments or cumulants. We apply the Maximum Entropy Method (MEM) and Gaussian Process (GP) to reconstruct net-baryon number distributions across the QCD chiral crossover region using cumulant data from the STAR experiment and functional renormalization group (fRG) calculations. Our results demonstrate how higher-order cumulants shape distribution tails, while anomalous features in the reconstructed distributions provide constraints on the input cumulants. We also discuss deep learning approaches for distribution reconstruction from cumulants and present our recent work on physics-informed neural networks (PINNs) for solving fRG equations.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Can AI understand Hamiltonian mechanics?
2025年1月31日(金) 16:00 - 17:00
Tae-Geun Kim (Ph.D. Student, Department of Physics, Yonsei University, Republic of Korea)
With recent breakthroughs in deep learning, particularly in areas like natural language processing and image recognition, AI has shown remarkable abilities in understanding complex patterns. This raises a fundamental question: Can AI grasp the core concepts of physics that govern the natural world? In this talk, as a first step towards addressing this question, we will discuss the possibility of AI understanding Hamiltonian mechanics. We will first introduce the concept of operator learning, a novel technique that allows AI to learn mappings between infinite-dimensional spaces, and its application to Hamiltonian mechanics by reformulating it within this framework. Then, we will test whether AI can derive trajectories in phase space given an arbitrary potential function, without relying on any equations or numerical solvers. We will then showcase our findings, demonstrating AI's capability to predict phase space trajectories under certain constraints. Finally, we will discuss the limitations, future research directions, and the potential for AI to contribute to scientific discovery.
会場: via Zoom
イベント公式言語: 英語
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セミナー
Stochastic Normalizing Flows for Lattice Field Theory
2024年12月18日(水) 15:30 - 16:30
Elia Cellini (PhD, Department of Physics, University of Turin, Italy)
Normalizing Flows (NFs) are a class of deep generative models that have recently been proposed as efficient samplers for Lattice Field Theory. Although NFs have demonstrated impressive performance in toy models, their scalability to larger lattice volumes remains a significant challenge, limiting their application to state-of-the-art problems. A promising approach to overcoming these scaling limitations involves combining NFs with non-equilibrium Markov Chain Monte Carlo (NEMCMC) algorithms, resulting in Stochastic Normalizing Flows (SNFs). SNFs harness the scalability of MCMC samplers while preserving the expressiveness of NFs. In this seminar, I will introduce the concepts of NEMCMC and NFs, demonstrate their combination into SNFs, and outline their connections with non-equilibrium thermodynamics. I will conclude by discussing key aspects of SNFs through their application to Effective String Theory, SU(3) gauge theory, and conformal field theory.
会場: セミナー室 (359号室) 3階 359号室とZoomのハイブリッド開催
イベント公式言語: 英語
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セミナー
How Neural Networks reduce the Fermionic Sign Problem and what we can learn from them
2024年12月11日(水) 15:30 - 16:30
Johann Ostmeyer (Post-doctoral Fellow, Helmholtz-Institut für Strahlen- und Kernphysik, University of Bonn, Germany)
When simulating fermionic quantum systems, non-perturbative Monte Carlo techniques are often the most efficient approach known to date. However, beyond half filling they suffer from the so-called sign problem, i.e. negative "probabilities", so that stochastic sampling becomes infeasible. Recently, considerable progress has been made in alleviating the sign problem by deforming the integration contour of the path integral into the complex plane and applying machine learning to find near-optimal alternative contours. In this talk, I am going to present a particularly successful architecture, based on complex-valued affine coupling layers. Furthermore, I will demonstrate how insight gained from the trained network can be used for simpler analytic approaches.
会場: via Zoom / セミナー室 (359号室) 3階 359号室とZoomのハイブリッド開催
イベント公式言語: 英語
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セミナー
Deep Learning for Non-Perturbative Quantum Chromodynamics
2024年12月4日(水) 15:00 - 16:30
Fu-Peng Li (PhD Candidate, Key Laboratory of Quark and Lepton Physics (MOE) and Institute of Particle Physics, Central China Normal University, China)
Machine learning, particularly deep learning, is revolutionizing research across diverse disciplines, including physics. In this seminar, we explore the application of deep learning techniques to tackle challenges in non-perturbative Quantum Chromodynamics (QCD), one of the most complex areas in fundmental physics. I will present our preliminary explorations in this interdisciplinary field, focusing on: (i) identifying the equations of state for nuclear matter, (ii) developing a neural network-based quasi-particle model for QCD equations of state, (iii) extracting parton fragmentation functions, and (iv) determining heavy quark interaction potentials. Fu-Peng Li s a Ph.D. candidate in Theoretical Physics at Central China Normal University(CCNU) with an expected graduation in June 2025. His research interests lie at the intersection of nuclear physics and machine learning, with a focus on auto-differentiation, physics-informed neural networks (PINNs) for inverse problems, and the application of machine learning to non-perturbative Quantum Chromodynamics (QCD).
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Solving inverse problem via latent variable optimization of diffusion models: An application to CT reconstruction
2024年11月25日(月) 14:00 - 15:00
尾崎 翔 (弘前大学 大学院理工学研究科 助教)
Inverse problems are widely studied in various scientific fields, including mathematics, physics, and medical imaging (such as CT and MRI reconstructions). In this talk, I will present a novel method for solving inverse problems using the diffusion model, with an application to CT reconstruction. The diffusion model, which is a core component of recent image-generative AI, such as Stable Diffusion and DALL-E3, is capable of producing high-quality images with rich diversity. The imaging process in CT (i.e., CT reconstruction) is mathematically an inverse problem. When the radiation dose is reduced to minimize a patient's exposure, image quality deteriorates due to information loss, making the CT reconstruction problem highly ill-posed. In the proposed method, the diffusion model, trained with a large dataset of high-quality images, serves as a regularization technique to address the ill-posedness. Consequently, the proposed method reconstructs high-quality images from sparse (low-dose) CT data while preserving the patient's anatomical structures. We also compare the performance of the proposed method with those of other existing methods, and find that the proposed method outperforms the existing methods in terms of quantitative indices.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Machine learning applications in neutron star physics
2024年11月19日(火) 15:00 - 16:30
Márcio Ferreira (Researcher, Physics Department, University of Coimbra, Portugal)
The equation of state and the internal composition of a neutron star are still unanswered questions in astrophysics. To constrain the different composition scenarios inside neutron stars, we rely on pulsars observations and gravitational waves detections. This seminar shows different applications of supervised/unsupervised machine learning models in neutron stars physics, such as: i) extract the equation of state; ii) infer the proton fraction; iii) detect the possible existence of a second branch in the mass-radius diagram; and iv) detect the presence of hyperons. Márcio Ferreira is a researcher at the Center for Physics at the University of Coimbra, Portugal, focusing on the application of machine learning to astrophysics and materials science. His work utilizes generative and descriptive models to address key questions in these fields. With a PhD in high energy physics and a Master’s in quantitative methods for finance, Márcio also merges his expertise in physics with an interest in financial market dynamics.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Understanding Diffusion Models by Feynman's Path Integral
2024年10月9日(水) 15:00 - 16:30
広野 雄士 (大阪大学 大学院理学研究科 物理学専攻 助教)
Diffusion models have emerged as powerful tools in generative modeling, especially in image generation tasks. In this talk, we introduce a novel perspective by formulating diffusion models using the path integral method introduced by Feynman for describing quantum mechanics. We find this formulation providing comprehensive descriptions of score-based diffusion generative models, such as the derivation of backward stochastic differential equations and loss functions for optimization. The formulation accommodates an interpolating parameter connecting stochastic and deterministic sampling schemes, and this parameter can be identified as a counterpart of Planck's constant in quantum physics. This analogy enables us to apply the Wentzel-Kramers-Brillouin (WKB) expansion, a well-established technique in quantum physics, for evaluating the negative log-likelihood to assess the performance disparity between stochastic and deterministic sampling schemes.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Renormalization Group Approach for Machine Learning Hamiltonian
2024年9月10日(火) 15:00 - 17:00
尾澤 岬 (CNRS Researcher, Laboratory for Interdisciplinary Physics (LIPhy), Université Grenoble Alpes, France)
We develop a multiscale approach to estimate high-dimensional probability distributions. Our approach applies to cases in which the energy function (or Hamiltonian) is not known from the start. Using data acquired from experiments or simulations we can estimate the underlying probability distribution and the associated energy function. Our method—the wavelet-conditional renormalization group (WCRG)—proceeds scale by scale, estimating models for the conditional probabilities of “fast degrees of freedom” conditioned by coarse-grained fields, which allows for fast sampling of many-body systems in various domains, from statistical physics to cosmology. Our method completely avoids the “critical slowing-down” of direct estimation and sampling algorithms. This is explained theoretically by combining results from RG and wavelet theories, and verified numerically for the Gaussian and φ4-field theories, as well as weak-gravitational-lensing fields in cosmology. Misaki Ozawa obtained his Ph.D. in 2015 from the University of Tsukuba. He did his first postdoc at the University of Montpellier in France. He then moved to Ecole Normale Supérieure (ENS) Paris as the second postdoc. Currently, he is a CNRS permanent researcher at Grenoble Alpes Univeristy in France. His background is in the physics of disordered systems such as glasses and spin glasses. He is also working on interdisciplinary studies between statistical physics and machine learning.
会場: セミナー室 (359号室) / via Zoom
イベント公式言語: 英語
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セミナー
Symmetries and Generalization for Machine Learning on a Lattice
2024年7月23日(火) 15:00 - 16:30
Andreas Ipp (Senior Scientist, Institute for Theoretical Physics, TU Wien, Austria)
Symmetries such as translations and rotations are crucial in physics and machine learning. The global symmetry of translations leads to convolutional neural networks (CNNs), while the much larger space of local gauge symmetry has driven us to develop lattice gauge equivariant convolutional neural networks (L-CNNs). This talk will discuss how the challenges of simulating the earliest stage of heavy ion collisions led us to use machine learning and how these innovations could improve lattice simulations in the future. Andreas Ipp is a Senior Scientist at the Institute for Theoretical Physics at TU Wien. He received his PhD in 2003 and held postdoctoral positions at ECT* in Trento and the Max-Planck-Institute in Heidelberg before returning to TU Wien in 2009. He completed his habilitation on "Yoctosecond dynamics of the quark-gluon plasma" in 2014. His current research focuses on symmetries in machine learning for applications in lattice gauge theory and heavy ion collisions.
会場: セミナー室 (359号室) (メイン会場) / via Zoom
イベント公式言語: 英語
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セミナー
Discovering Physical Laws with Artificial Intelligence
2024年7月12日(金) 10:00 - 11:30
刘 子鸣 (Ph.D. Student, Department of Physics, Massachusetts Institute of Technology, USA)
Deep neural networks have been extremely successful in language and vision tasks. However, their black-box nature makes them undesirable for scientific tasks. In this talk, I will show how we can make these black-box AI models more interpretable and transparent and use them to discover physical laws, including conservation laws (AI Poincare), symmetries, phase transitions and symbolic relations (Kolmogorov-Arnold Networks). Ziming is a physicist and a machine learning researcher. Ziming received BS in physics from Peking Univeristy in 2020, and is current a fourth-year PhD student at MIT and IAIFI, advised by Max Tegmark. His research interests lie generally in the intersection of artificial intelligence (AI) and physics (science in general).
会場: via Zoom
イベント公式言語: 英語
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セミナー
Inferring collective behavior from social interactions to population coding
2024年6月27日(木) 16:00 - 17:30
Chen Xiaowen (Postdoctoral Researcher, Laboratoire de Physique de l’École normale supérieure, CNRS, France)
(This is a joint iTHEMS Biology Seminar) From social animals to neuronal networks, collective behavior is ubiquitous in living systems. How are these behaviors encoded in interactions, and how do they drive biological functions? Recent insights from statistical physics applied to biological data have offer exciting new perspectives. However, previous research has mostly focused on the statics, i.e. the steady-state distributions of the collective behavior, without taking into consideration of time. In this talk, I will present two recent progresses tapping into the temporal domain. First, I will present a study of collective behavior in social mice from their co-localization patterns. To capture both static and dynamic features of the data, we developed a novel inference method termed the generalized Glauber dynamics (GGD) that can tune the dynamics while keeping the steady state distribution fixed. I will first outline the explanation power of the GGD dynamics, then explain how to infer the dynamics from data. The inferred interactions characterize sociability for different mice strains. In the second example, we studied information flow among neurons in the larval zebrafish hindbrain. By adapting the method of Granger causality to single cell calcium transient data, we were able to detect both a global information flow among neurons, as well as identifying brain regions that are key in locomotion.
会場: via Zoom
イベント公式言語: 英語
17 イベント