arXiv subjects

Takashi Maruyama

Publications and source records attributed to Takashi Maruyama.

18 recordsLinked to original sources

arXiv2026

Hyperspatial Sampling: Circumventing Free-Energy Barriers via Replica Exchange with Extra Dimensions

Simulating systems with rugged free-energy landscapes remains a central challenge in computational physics and chemistry. We introduce hyperspatial replica exchange (HS-REX), an enhanced sampling method in which the physical system is artificially extended by additional spatial dimensions. In higher dimensions, free-energy barriers can be circumvented through paths that are geometrically inaccessible in the original space. Restricting the penalty to only solute atoms dramatically reduces the number of replicas required for solvated systems compared to standard temperature replica exchange, a feature especially relevant for biological applications. As proof of concept, we demonstrate the method on a double-well model system and on alanine dipeptide in explicit water as benchmark system. In the latter case, HS-REX achieves enhanced conformational sampling of not only the slow backbone dihedral angles, but also both chiral configurations of the molecule, which are sterically inaccessible to standard sampling in three dimensions. This demonstrates enhanced ergodic sampling over conventional temperature replica exchange.

physics.comp-ph↗

arXiv2026

A Leibniz rule of distributional pairing and hyperforce sum rule

We reformulate and generalize the equilibrium hyperforce sum rule, a generalization of the Bogoliubov-Born-Green-Kirkwood-Yvon (BBGKY) hierarchy, by employing the Schwartz space and its dual. We show that the hyperforce sum rule for the Euclidean space and the equilibrium BBGKY hierarchy at arbitrary level are derived through the Leibniz rule of the derivative for the pairing of tempered distributions and Schwartz functions. We also apply the Leibniz rule to obtain the hyperforce sum rule for systems with periodic boundary conditions.

math-ph↗

arXiv2025

Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations

We present an end-to-end differentiable molecular simulation framework (DIMOS) for molecular dynamics and Monte Carlo simulations. DIMOS easily integrates machine-learning-based interatomic potentials and implements classical force fields including an efficient implementation of particle-mesh Ewald. Thanks to its modularity, both classical and machine-learning-based approaches can be easily combined into a hybrid description of the system (ML/MM). By supporting key molecular dynamics features such as efficient neighborlists and constraint algorithms for larger time steps, the framework makes steps in bridging the gap between hand-optimized simulation engines and the flexibility of a \verb|PyTorch| implementation. We show that due to improved linear instead of quadratic scaling as function of system size DIMOS is able to obtain speed-up factors of up to $170\times$ for classical force field simulations against another fully differentiable simulation framework. The advantage of differentiability is demonstrated by an end-to-end optimization of the proposal distribution in a Markov Chain Monte Carlo simulation based on Hamiltonian Monte Carlo (HMC). Using these optimized simulation parameters a $3\times$ acceleration is observed in comparison to ad-hoc chosen simulation parameters. The code is available at https://github.com/nec-research/DIMOS.

physics.comp-ph↗

arXiv2025

Geometric Kolmogorov-Arnold Superposition Theorem

The Kolmogorov-Arnold Theorem (KAT), or more generally, the Kolmogorov Superposition Theorem (KST), establishes that any non-linear multivariate function can be exactly represented as a finite superposition of non-linear univariate functions. Unlike the universal approximation theorem, which provides only an approximate representation without guaranteeing a fixed network size, KST offers a theoretically exact decomposition. The Kolmogorov-Arnold Network (KAN) was introduced as a trainable model to implement KAT, and recent advancements have adapted KAN using concepts from modern neural networks. However, KAN struggles to effectively model physical systems that require inherent equivariance or invariance geometric symmetries as $E(3)$ transformations, a key property for many scientific and engineering applications. In this work, we propose a novel extension of KAT and KAN to incorporate equivariance and invariance over various group actions, including $O(n)$, $O(1,n)$, $S_n$, and general $GL$, enabling accurate and efficient modeling of these systems. Our approach provides a unified approach that bridges the gap between mathematical theory and practical architectures for physical systems, expanding the applicability of KAN to a broader class of problems. We provide experimental validation on molecular dynamical systems and particle physics.

cs.LG↗

arXiv2025

Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching

Long-range interactions are essential for the correct description of complex systems in many scientific fields. The price to pay for including them in the calculations, however, is a dramatic increase in the overall computational costs. Recently, deep graph networks have been employed as efficient, data-driven models for predicting properties of complex systems represented as graphs. These models rely on a message passing strategy that should, in principle, capture long-range information without explicitly modeling the corresponding interactions. In practice, most deep graph networks cannot really model long-range dependencies due to the intrinsic limitations of (synchronous) message passing, namely oversmoothing, oversquashing, and underreaching. This work proposes a general framework that learns to mitigate these limitations: within a variational inference framework, we endow message passing architectures with the ability to adapt their depth and filter messages along the way. With theoretical and empirical arguments, we show that this strategy better captures long-range interactions, by competing with the state of the art on five node and graph prediction datasets.

cs.LG↗

arXiv2025

Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction

Knowledge graph embedding (KGE) models encode the structural information of knowledge graphs to predicting new links. Effective training of these models requires distinguishing between positive and negative samples with high precision. Although prior research has shown that improving the quality of negative samples can significantly enhance model accuracy, identifying high-quality negative samples remains a challenging problem. This paper theoretically investigates the condition under which negative samples lead to optimal KG embedding and identifies a sufficient condition for an effective negative sample distribution. Based on this theoretical foundation, we propose \textbf{E}mbedding \textbf{MU}tation (\textsc{EMU}), a novel framework that \emph{generates} negative samples satisfying this condition, in contrast to conventional methods that focus on \emph{identifying} challenging negative samples within the training data. Importantly, the simplicity of \textsc{EMU} ensures seamless integration with existing KGE models and negative sampling methods. To evaluate its efficacy, we conducted comprehensive experiments across multiple datasets. The results consistently demonstrate significant improvements in link prediction performance across various KGE models and negative sampling methods. Notably, \textsc{EMU} enables performance improvements comparable to those achieved by models with embedding dimension five times larger. An implementation of the method and experiments are available at https://github.com/nec-research/EMU-KG.

cs.LG↗

arXiv2024

Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing

The ability to perform fast and accurate atomistic simulations is crucial for advancing the chemical sciences. By learning from high-quality data, machine-learned interatomic potentials achieve accuracy on par with ab initio and first-principles methods at a fraction of their computational cost. The success of machine-learned interatomic potentials arises from integrating inductive biases such as equivariance to group actions on an atomic system, e.g., equivariance to rotations and reflections. In particular, the field has notably advanced with the emergence of equivariant message passing. Most of these models represent an atomic system using spherical tensors, tensor products of which require complicated numerical coefficients and can be computationally demanding. Cartesian tensors offer a promising alternative, though state-of-the-art methods lack flexibility in message-passing mechanisms, restricting their architectures and expressive power. This work explores higher-rank irreducible Cartesian tensors to address these limitations. We integrate irreducible Cartesian tensor products into message-passing neural networks and prove the equivariance and traceless property of the resulting layers. Through empirical evaluations on various benchmark data sets, we consistently observe on-par or better performance than that of state-of-the-art spherical and Cartesian models.

cs.LG↗

arXiv2024

A Young-type integration on self-similar sets in intervals

We introduce a generalization of the Young integration on self-similar sets defined in a closed interval and give a sufficient condition of its integrability. We also prove integration by substitution, integration by parts and term-by-term integration and give examples of the properties.

math.FA↗

arXiv2024

Compositional Generative Inverse Design

Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem that arises across fields such as mechanical engineering to aerospace engineering. Inverse design is typically formulated as an optimization problem, with recent works leveraging optimization across learned dynamics models. However, as models are optimized they tend to fall into adversarial modes, preventing effective sampling. We illustrate that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples and significantly improve design performance. We further illustrate how such a design system is compositional, enabling us to combine multiple different diffusion models representing subcomponents of our desired system to design systems with every specified component. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes that are more complex than those in the training data. Our method generalizes to more objects for N-body dataset and discovers formation flying to minimize drag in the multi-airfoil design task. Project website and code can be found at https://github.com/AI4Science-WestlakeU/cindm.

cs.LG↗

arXiv2023

Learning Controllable Adaptive Simulation for Multi-resolution Physics

Simulating the time evolution of physical systems is pivotal in many scientific and engineering problems. An open challenge in simulating such systems is their multi-resolution dynamics: a small fraction of the system is extremely dynamic, and requires very fine-grained resolution, while a majority of the system is changing slowly and can be modeled by coarser spatial scales. Typical learning-based surrogate models use a uniform spatial scale, which needs to resolve to the finest required scale and can waste a huge compute to achieve required accuracy. In this work, we introduce Learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model that jointly learns the evolution model and optimizes appropriate spatial resolutions that devote more compute to the highly dynamic regions. LAMP consists of a Graph Neural Network (GNN) for learning the forward evolution, and a GNN-based actor-critic for learning the policy of spatial refinement and coarsening. We introduce learning techniques that optimizes LAMP with weighted sum of error and computational cost as objective, allowing LAMP to adapt to varying relative importance of error vs. computation tradeoff at inference time. We evaluate our method in a 1D benchmark of nonlinear PDEs and a challenging 2D mesh-based simulation. We demonstrate that our LAMP outperforms state-of-the-art deep learning surrogate models, and can adaptively trade-off computation to improve long-term prediction error: it achieves an average of 33.7% error reduction for 1D nonlinear PDEs, and outperforms MeshGraphNets + classical Adaptive Mesh Refinement (AMR) in 2D mesh-based simulations. Project website with data and code can be found at: http://snap.stanford.edu/lamp.

cs.LG↗

arXiv2023

A combinatorial Fredholm module on self-similar sets built on $n$-cubes

We construct a Fredholm module on self-similar sets such as the Cantor dust, the Sierpinski carpet and the Menger sponge. Our construction is a higher dimensional analogue of Connes' combinatorial construction of the Fredholm module on the Cantor set. We also calculate the Dixmier trace of two operators induced by the Fredholm module.

math.KT↗

arXiv2022

Learning to Accelerate Partial Differential Equations via Latent Global Evolution

Simulating the time evolution of Partial Differential Equations (PDEs) of large-scale systems is crucial in many scientific and engineering domains such as fluid dynamics, weather forecasting and their inverse optimization problems. However, both classical solvers and recent deep learning-based surrogate models are typically extremely computationally intensive, because of their local evolution: they need to update the state of each discretized cell at each time step during inference. Here we develop Latent Evolution of PDEs (LE-PDE), a simple, fast and scalable method to accelerate the simulation and inverse optimization of PDEs. LE-PDE learns a compact, global representation of the system and efficiently evolves it fully in the latent space with learned latent evolution models. LE-PDE achieves speed-up by having a much smaller latent dimension to update during long rollout as compared to updating in the input space. We introduce new learning objectives to effectively learn such latent dynamics to ensure long-term stability. We further introduce techniques for speeding-up inverse optimization of boundary conditions for PDEs via backpropagation through time in latent space, and an annealing technique to address the non-differentiability and sparse interaction of boundary conditions. We test our method in a 1D benchmark of nonlinear PDEs, 2D Navier-Stokes flows into turbulent phase and an inverse optimization of boundary conditions in 2D Navier-Stokes flow. Compared to state-of-the-art deep learning-based surrogate models and other strong baselines, we demonstrate up to 128x reduction in the dimensions to update, and up to 15x improvement in speed, while achieving competitive accuracy.

cs.LG↗

arXiv2022

A combinatorial integration on the Cantor dust

In this paper, we generalize the Cantor function to $2$-dimensional cubes and construct a cyclic $2$-cocycle on the Cantor dust. This cocycle is non-trivial on the pullback of the smooth functions on the $2$-dimensional torus with the generalized Cantor function while it vanishes on the Lipschitz functions on the Cantor dust. The cocycle is calculated through the integration of $2$-forms on the torus by using a combinatorial Fredholm module.

math.KT↗

arXiv2022

The Heavy Photon Search Experiment

The Heavy Photon Search (HPS) experiment is designed to search for a new vector boson $A^\prime$ in the mass range of 20 MeV/$c^2$ to 220 MeV/$c^2$ that kinetically mixes with the Standard Model photon with couplings $ε^2 >10^{-10}$. In addition to the general importance of exploring light, weakly coupled physics that is difficult to probe with high-energy colliders, a prime motivation for this search is the possibility that sub-GeV thermal relics constitute dark matter, a scenario that requires a new comparably light mediator, where models with a hidden $U(1)$ gauge symmetry, a "dark", "hidden sector", or "heavy" photon, are particularly attractive. HPS searches for visible signatures of these heavy photons, taking advantage of their small coupling to electric charge to produce them via a process analogous to bremsstrahlung in a fixed target and detect their subsequent decay to $\mathrm{e}^+ \mathrm{e}^-$ pairs in a compact spectrometer. In addition to searching for $\mathrm{e}^+ \mathrm{e}^-$ resonances atop large QED backgrounds, HPS has the ability to precisely measure decay lengths, resulting in unique sensitivity to dark photons, as well as other long-lived new physics. After completion of the experiment and operation of engineering runs in 2015 and 2016 at the JLab CEBAF, physics runs in 2019 and 2021 have provided datasets that are now being analyzed to search for dark photons and other new phenomena.

hep-ex↗

arXiv2018

Light Dark Matter eXperiment (LDMX)

We present an initial design study for LDMX, the Light Dark Matter Experiment, a small-scale accelerator experiment having broad sensitivity to both direct dark matter and mediator particle production in the sub-GeV mass region. LDMX employs missing momentum and energy techniques in multi-GeV electro-nuclear fixed-target collisions to explore couplings to electrons in uncharted regions that extend down to and below levels that are motivated by direct thermal freeze-out mechanisms. LDMX would also be sensitive to a wide range of visibly and invisibly decaying dark sector particles, thereby addressing many of the science drivers highlighted in the 2017 US Cosmic Visions New Ideas in Dark Matter Community Report. LDMX would achieve the required sensitivity by leveraging existing and developing detector technologies from the CMS, HPS and Mu2e experiments. In this paper, we present our initial design concept, detailed GEANT-based studies of detector performance, signal and background processes, and a preliminary analysis approach. We demonstrate how a first phase of LDMX could expand sensitivity to a variety of light dark matter, mediator, and millicharge particles by several orders of magnitude in coupling over the broad sub-GeV mass range.

hep-ex↗

arXiv2016

Cyclic Cohomology Groups of Some Self-similar Sets

We define a variant of the Young integration on some kinds of self-similar sets which are called cellular self-similar sets. This variant is an analogue of the Young integration defined on the unit interval. We give the criteria of the variant on cellular self-similar sets, and also show that the variant is a cyclic 1-cocycle of the algebra of complex-valued Hölder continuous functions on the cellular self-similar sets. This suggests that the cocycle is a variant of currents.

math.MG↗

arXiv2015

The Heavy Photon Search Test Detector

The Heavy Photon Search (HPS), an experiment to search for a hidden sector photon in fixed target electroproduction, is preparing for installation at the Thomas Jefferson National Accelerator Facility (JLab) in the Fall of 2014. As the first stage of this project, the HPS Test Run apparatus was constructed and operated in 2012 to demonstrate the experiment's technical feasibility and to confirm that the trigger rates and occupancies are as expected. This paper describes the HPS Test Run apparatus and readout electronics and its performance. In this setting, a heavy photon can be identified as a narrow peak in the e$^+$e$^-$ invariant mass spectrum, above the trident background or as a narrow invariant mass peak with a decay vertex displaced from the production target, so charged particle tracking and vertexing are needed for its detection. In the HPS Test Run, charged particles are measured with a compact forward silicon microstrip tracker inside a dipole magnet. Electromagnetic showers are detected in a PbW0$_{4}$ crystal calorimeter situated behind the magnet, and are used to trigger the experiment and identify electrons and positrons. Both detectors are placed close to the beam line and split top-bottom. This arrangement provides sensitivity to low-mass heavy photons, allows clear passage of the unscattered beam, and avoids the spray of degraded electrons coming from the target. The discrimination between prompt and displaced e$^+$e$^-$ pairs requires the first layer of silicon sensors be placed only 10~cm downstream of the target. The expected signal is small, and the trident background huge, so the experiment requires very large statistics. Accordingly, the HPS Test Run utilizes high-rate readout and data acquisition electronics and a fast trigger to exploit the essentially 100% duty cycle of the CEBAF accelerator at JLab.

physics.ins-det↗

arXiv2002

Polarized Photocathodes Make the Grade

Future linear colliders will require high levels of performance from their electron sources. A group at SLAC has recently tested a structure that substantially exceeds current collider polarized electron source pulse-profile requirements.

physics.acc-ph↗