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.

The stochastic process is a popular tool for broad disciplines, such as physics, biophysics, chemistry, neuroscience, economics, and finance. In this lecture course, I will provide an elementary introduction to stochastic processes in physics without assuming rigorous background knowledge of probability theories. Most of the basic topics in stochastic processes will be covered in this lecture course, such as (1) the one-to-one correspondence between stochastic differential equations and master equations, (2) their standard forms, (3) Ito's lemma, and (4) the perturbation theories (the system-size expansion). I will also present its application to statistical physics, such as (5) kinetic theory and (6) a microscopic derivation of the Langevin equation from hard-sphere Hamiltonian dynamics in the dilute gas limit. My goal is to help the audience calculate most of the main calculations by their own hands by providing detailed explanations without abbreviations. This lecture is based on my Japanese notebook, available on my webpage (see the link below).

Schedule:
(Tue., Feb. 4) 13:00-14:30, 14:45-16:15, 16:30-18:00
(Wed., Feb. 5) 10:30-12:00, 13:00-14:30, 14:45-16:15

The Markovian process is one of the most important classes of stochastic processes. The Markovian process is defined as a stochastic process whose time evolution is independent of the system's entire history and has been extensively studied using the master equation and Fokker-Planck equation approaches. In contrast, non-Markovian processes -- where time evolution depends on the full history of the system -- have not been systematically explored, except for a few special cases, such as semi-Markovian processes. In this talk, we present a recent master-equation approach to general non-Markovian jump processes [1-4]. Beginning with a general non-Markovian jump process, we derive the corresponding master equation through a Markovian-embedding approach. The Markovian embedding is a scheme to add a sufficient number of auxiliary variables to convert a non-Markovian model to a high-dimensional Markovian model. For the case of our model, the one-dimensional non-Markovian model is shown to be equivalent to a Markovian stochastic field theory, and we derive the field master equation correspondingly [4]. As an application, we examine the nonlinear Hawkes process, a history-dependent and self-exciting model frequently used in studying complex systems [1-3]. Additionally, we explore the stochastic thermodynamic framework for general jump processes [5] as another example.

In recent years, the rapid development of large language models (LLMs) such as ChatGPT has given many researchers a strong impression that these systems truly exhibit “intelligence.” In this presentation, we first review the evolution of AI research, explaining how large language models go beyond conventional machine learning by enabling more “general” forms of learning. We then highlight the importance of “sensors” and “mathematical capability” as key factors that allow AI to autonomously carry out scientific tasks such as problem analysis, hypothesis generation, and proofs in fields like mathematics and physics. We also examine how proof assistants can address the issue of hallucinations in LLM outputs, and discuss the role of combinatorial creativity in accelerating interdisciplinary research. Finally, we introduce our “AI Mathematician” agent project, demonstrating how integrating large language models with proof assistants can open new horizons in mathematical sciences.

Protein-RNA condensates are involved in a range of cellular activities. Coarse-grained molecular models of intrinsically disordered proteins have been developed to shed light on and predict single-chain properties and phase separation. An RNA model compatible with such models for disordered proteins would enable the study of complex biomolecular mixtures involving RNA. Here, we present a sequence-independent coarse-grained, two-bead-per-nucleotide model of disordered, flexible RNA based on a hydropathy scale. We parameterize the model, which we term CALVADOS-RNA, using a combination of bottom-up and top-down approaches to reproduce local RNA geometry and intramolecular interactions based on atomistic simulations and in vitro experiments. The model semi-quantitatively captures several aspects of RNA-RNA and RNA-protein interactions. We examined RNA-RNA interactions by comparing calculated and experimental virial coefficients, and non-specific RNA-protein interaction by studying reentrant phase behavior of protein-RNA mixtures. We demonstrate the utility of the model by simulating the formation of mixed condensates consisting of the disordered region of MED1 and RNA chains, and the selective partitioning of disordered regions from transcription factors into these, and compare the results to experiments. Despite the simplicity of our model we show that it captures several key aspects of protein-RNA interactions and may therefore be used as a baseline model to study several aspects of the biophysics and biology of protein-RNA condensates.

Neutron stars in low-mass X-ray binaries, accreting hydrogen- or helium-rich material from a companion star, frequently exhibit thermonuclear runaways on their surfaces known as Type-I X-ray bursts (XRBs). These bursts are powered by nuclear processes, such as the triple-$\alpha$ process, the $\alpha p$ process, and the rapid proton capture process, which play a critical role in model-observation comparisons. In this study, we investigate the impact of nuclear reaction uncertainties on XRBs using the ONEZONE model (Cyburt et al., 2016), considering different accreted compositions and accretion rates for the binary systems that are within the range of observed burst sources. The study is carried out in two stages. First, we determine the burst ignition conditions by simulating the settling of the accreted material with a full reaction network and a semi-analytical model. Second, we perform a sensitivity analysis by varying proton- and alpha-induced reaction rates in JINA REACLIBV2.2 within their estimated uncertainties. We explore the influence of these reactions on the XRBs light curve and the final abundances. The findings highlight key nuclear reactions that significantly affect XRB observables and the final abundances produced, offering guidance for future experimental efforts to improve our understanding of the uncertainties in the reaction rates involved in XRBs.

This seminar investigates the Urca cooling strength of the 63Fe-63Mn pair, which varies due to uncertainties in the spin-parity of 63Fe, relevant to the Island of Inversion at N = 40. We present simulations that analyze the impact of this cooling mechanism on the thermal evolution of neutron star crusts, focusing on superburst ignition and anomalous hot quiescent phase cooling of MAXI J0556-332. Additionally, we explore the potential crust Urca process through the anomalous cooling curve of MAXI J0556-332, fitting observational data to determine neutron star mass and radius preferences. Preliminary results suggest that neutron stars with a crust Urca process tend to have smaller masses and larger radii, highlighting the need for precise β-decay measurements to further understand these phenomena.

Research on planet formation involves various approaches, including explorations of small solar system bodies, observations of protoplanetary disks, dust experiments, simulations, and theoretical studies. One of the primary objectives in this field is to develop a comprehensive theory that explains how kilometer-sized planetesimals form from micrometer-sized dust grains, drawing upon findings from these diverse research methods.

This workshop will focus on the concept of pebbles, which play a crucial role in the planet formation process. Pebbles — typically defined as solids ranging from millimeter to centimeter in size — are intermediate building blocks in planet formation, though their definition varies depending on the context. Assuming pebbles has led to theoretical advances in mechanisms such as streaming instability and pebble accretion, which promote the formation and growth of planetesimals. Additionally, pebbles have been linked to barriers against dust growth, such as the bouncing barrier. Furthermore, observations of protoplanetary disks have revealed the size distribution and porosity of solids, while the strength and thermal conductivity of comets obtained by the Rosetta mission suggest the accumulation of pebbles due to disk instabilities. However, inconsistencies have been pointed out between pebble formation and theories of dust growth.

This workshop aims to revisit and refine our understanding of solid materials implicated in planet formation, particularly in light of findings from solar system explorations and protoplanetary disk observations. We aim to reevaluate the definition and role of pebbles in the broader context of planet formation, with a special focus on the current challenges and open questions in the field. The workshop will include discussions of experiments and simulations of dust growth and collisions, and planetesimal formation mechanisms such as streaming instability. The workshop features keynote talks from the perspectives of explorations, observations, experiments, simulations, and theories, and we also call for presentations on related topics.

14:45-15:00 Teatime discussion

[15:00-16:30 First part: MACS 10th Anniversary Colloquium]
15:05-15:05 Opening
15:05-15:30 Talk by Prof. Hiroshi KOKUBU
Title: How did MACS begin?
Abstract: As the MACS program, which began with a kick-off symposium in May 2016, enters its 10th year in the academic year 2025, I would like to look it back and talk about how it started, what thoughts shared by people involved at the time led to the spirit of MACS. I’d also like to share ideas and experiences in the history of MACS over the past 10 years, including what we wanted to do with MACS in the beginning but could not, or how MACS have collaborated with other subsequent activities of Kyodai RIGAKU (Kyoto U Science).
15:30-15:55 Talk by Prof. Yoshiko TAKAHASHI
Title: Excitement through the MACS program
Abstract: When the MACS program was launched, a research article was published by Harvard University, in which the gut looping during vertebrate development was beautifully explained by inter-disciplined science with experimental biology, physics, and mathematics. I was very impressed and motivated by this paper, and aimed at similar new waves through the MACS program. I have been running a study group, in which graduate- and undergrad students of not only life science but also physics and mathematics joined, and we enjoyed discussion and looking at real chicken embryos. Such experiences are not what we can easily obtain in conventional education program in campus life.

15:55-16:20 Discussion
16:20-16:30 Break

[16:30-18:30 Second part: 2024 MACS Achievement Report Meeting]
16:30-17:30 Flash Talks to report results
17:30-18:30 Poster Session by SG participating students

It is fundamental in particle physics if the neutrino is a Dirac fermion or a Majorana fermion, and the seesaw model gives naturally a Majorana
neutrino in an extension of the Standard Model. However, the commonly used chirality changing $pseudo-C symmetry$ $\nu^{\tilde C}_L=C\overline{\nu_L}^T$
of a chiral fermion is not defined in Lagrangian field theory. Precisely speaking, the neutrinoless double beta decay is not described by the pseudo-C symmetry. The Majorana neutrino obtained after a Bogoliubov-type canonical transformation, which is the one originally defined by Majorana using a Dirac-type fermion, describes
consistently all the properties expected for the Majorana neutrino. Physical implication of this fact is briefly discussed.

iTHEMS Cosmology Forum Workshop is a series of short workshops, each focusing on an emerging topics in cosmology. The target audience is cosmologists, high-energy physicists and astronomers interested in learning about the subject, not just those who have already worked on the topic. The goal of the workshop is to provide working knowledge of the topic and leave dedicated time for discussions to encourage mutual interactions among participants.

The third workshop is devoted to the 'reheating' phase of the early Universe. Reheating bridges the gap between the (almost) empty universe at the end of cosmic inflation and the thermal state of particles, required for Big-Bang nucleosynthesis, and the events of the hot Big-Bang model as a whole, to unfold. It is expected to proceed in different stages starting with a violent parametric resonant creation of particles, dubbed preheating, followed by a redistribution of energy leading to a thermal state. This phase potentially hosts rich phenomenology such as the formation of topoligical defects e.g. solitons, generation of gravitiational wave, and so on. Yet, the very non-linear nature of reheating makes it notoriously hard to describe analytically, and even numerical simulations struggle to follow the whole sequence of events in a given model. Reheating studies have thus yet to reach the degree of compherensiveness and universality that the understanding of cosmic inflation has achieved.

This forum will consist of two events. The first, on March 4th, will be in conference format comprising scientific talks on research trends in (P)Reheating. The second, on March 5th, will be a tutorial on numerical aspects of reheating (both theory and hands-on with code) hosted by Tom Giblin of Kenyon College.

The workshop will be in English.

The workshops are organised by the iTHEMS Cosmology Forum working group, which is the successor of the Dark Matter Working Group at RIKEN iTHEMS.

Important dates:
Feb. 25th - Registration deadline
March 4th - Workshop Day (Room #435-437, Main Research Building 4F)
March 5th - Tutorial Day (Room #445-447, Main Research Building 4F)

Invited Speakers:
John T. Giblin - Kenyon College
Kyohei Mukaida - KEK
Seishi Enomoto - Yokohama National University

Organisers:
Kohei Hayashi, Nagisa Hiroshima, Derek Inman, Amaury Micheli, Ryo Namba

Although we rarely question how smart unicellular organisms are, it has become clear that unicellular organisms are smarter than we expected. In fact, various protozoa (unicellular eukaryotes) can take actions that are advantageous for their survival even in complex environments in the wild environments. In this talk, I will introduce some typical examples of smart behaviors in a protozoan amoeba (the plasmodium of Physarum polycephalum): (1) maze-solving, (2) formation of multi-functional transport network that mimics public transportation network among cities in Tokyo region, and so on. We will propose a mathematical model of these behaviors and extract the heuristics (simple rules of behavior) that give rise to their smartness. In general, we will discuss the future potential of research into the behavioral intelligence of protozoa.

The density functional theory (DFT) is one of the powerful methods to solve quantum many-body problems, which, in principle, gives the exact energy and density of the ground state. The accuracy of DFT is, in practice, determined by the accuracy of an energy density functional (EDF) since the exact EDF is still unknown. Currently, DFT has been used in many communities, including nuclear physics, quantum chemistry, and condensed matter physics, while the fundamental study of DFT, such as the first principle derivations of an accurate EDF and methods to calculate many observables from obtained densities and excited states, is still ongoing. However, there has been little opportunity to have interdisciplinary communication.

On December 2022, we had the first workshop on this series (DFT2022) at Yukawa Institute for Theoretical Physics, Kyoto University, and several interdisciplinary discussions and collaborations were started. On February 2024, we had the second workshop on this series (DFT2024) at RIKEN Kobe Campus, and more stimulated discussion occured. To keep and extend collaborations, we organize the third workshop. Since the third workshop, we extend the scope of the workshop to the development and application of DFT as well. In this workshop, the current status and issues of each discipline will be shared towards solving these problems by meeting together among researchers in mathematics, nuclear physics, quantum chemistry, and condensed matter physics.

This workshop mainly comprises lectures/seminars on cutting-edge topics and discussion, while sessions composed of contributed talks are also planned.