arXiv research

Deep-learning CNN automates Cu alloy grain size evaluation.

ML predicts alloy properties considering chemistry, processing, and data transformations.

The paper proves conditions for non-uniform expansion in partially hyperbolic systems.

Study improves materials discovery for high-entropy alloys using sparse linear models.

The paper studies the cut locus of submanifolds in Riemannian manifolds, providing geometric and topological insights.

The need for advanced materials has led to the development of complex, multi-component alloys or solid-solution alloys. These materials have shown exceptional properties like strength, toughness, ductility, electrical and electronic properties. Current development of such material systems are hindered by expensive expe…

CURIE uses cellular automata to detect concept drift in data streams.

Bayesian optimization identifies optimal alloy formulations.

In conventional chemisorption model, the d-band center theory (augmented sometimes with the upper edge of d-band for imporved accuarcy) plays a central role in predicting adsorption energies and catalytic activity as a function of d-band center of the solid surfaces, but it requires density functional calculations that…

This work tackles uncertainty in multi-agent multi-modal trajectory forecasting.

Method reveals dissimilarity in alloys' Curie temperatures.

Study uses AI and ML to predict and optimize corrosion resistance of aluminum alloys.

High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially characterize the degree of chemical ordering and identify order-disorder transitions through efficient simulation and modeling of thermodynamics. I…

The paper connects cut locus, Thom space, and Morse-Bott functions in Riemannian geometry.

In this short note, we consider gradient estimates for positive solutions to the following nonlinear elliptic equation on a complete Riemannian manifold: $Δu+cu^α=0,$ where $c, α$ are two real constants and $c\neq 0$.

There is considerable debate whether the domestic political institutions (specifically, the country s level of democracy) of the host developing country toward foreign investors are effective in establishing the credibility of commitments are still underway, researchers have also analyzed the effect of international in…

HAL accelerates the generation of training sets for accurate interatomic potentials.

In this paper we present some families of polynomials and use them to find, using the techniques in \cite{gma}, a defining polynomial for the $SL(2,\mathbb{C})$ character variety (as defined in \cite{cus}) of the torus knots of type $(m,2)$ with $m>1$ being an odd integer.

Model predicts EMF of Ni-Mn-Ga MSMA, improved with GRNN.

Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of complex material systems by integrating material science and design automation. For tractable ICME, it is required that (1) a structural feature space be identified to allow reconstruction of new designs, and (2) the reconstructi…

We propose a new \cu{class-optimal} algorithm for the distributed computation of Wasserstein Barycenters over networks. Assuming that each node in a graph has a probability distribution, we prove that every node can reach the barycenter of all distributions held in the network by using local interactions compliant with…

We construct a Hopf action, with an invariant trace, of a bicrossed product Hopf algebra $\cH=\big( \cU(\Fg_1) \acr \cR(G_2) \big)^{\cop}$ constructed from a matched pair of Lie groups $G_1$ and $G_2$, on a convolution algebra $\cA=C_c^{\ify}(G_1)\rtimes G_2^δ$. We give an explicit way to construct Hopf cyclic cohomolo…

We first analyze the integrated density of states (IDS) of periodic Schrödinger operators on an amenable covering manifold. A criterion for the continuity of the IDS at a prescribed energy is given along with examples of operators with both continuous and discontinuous IDS'. Subsequently, alloy-type perturbations of th…

We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully. We call our architeture LEAP net, for Latent Encoding of Atypical Perturbation. Our method implements…

Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible. Existing methods depend either on limited active variables or the additive form of the objective function. We propose a new method for high-dime…

In this paper, we will address to the following parabolic equation $u_t=Δ_fu + F(u)$ on a smooth metric measure space with Bakry-Émery curvature bounded from below. Here $F$ is a differentiable function defined in $\mathbb{R}$. Our motivation is originally inspired by gradient estimates of Allen-Cahn and Fisher equ…

The paper estimates gradients on graphs under specific conditions and applies these estimates to heat equations.

We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class. Prototypical networks learn a metric space in which classification can be performed by computing distances…

The paper presents a novel approach to direct covariance function learning for Bayesian optimisation, with particular emphasis on experimental design problems where an existing corpus of condensed knowledge is present. The method presented borrows techniques from reproducing kernel Banach space theory (specifically m-k…

The paper studies gradient estimates and Liouville theorems for a nonlinear elliptic equation on metric measure spaces.

The paper analyzes market risk factors for a mining company using a VAR model with stable distribution.

A Laguerre minimal surface is an immersed surface in the Euclidean space being an extremal of the functional \int (H^2/K - 1) dA. In the present paper, we prove that the only ruled Laguerre minimal surfaces are up to isometry the surfaces R(u,v) = (Au, Bu, Cu + D cos 2u) + v (sin u, cos u, 0), where A, B, C, D are fixe…

Global gradient estimates for Fisher-KPP equation on Finsler metric measure spaces.

Let $(X^{n+1}, g^+)$ be an $(n+1)$-dimensional asymptotically hyperbolic manifold with a conformal infinity $(M^n, [\hat{h}])$. The fractional Yamabe problem addresses to solve \[P^γ[g^+,\hat{h}] (u) = cu^{n+2γ\over n-2γ}, \quad u > 0 \quad \text{on } M\] where $c \in \mathbb{R}$ and $P^γ[g^+,\hat{h}]$ is the fractiona…

Neural Network is a powerful Machine Learning tool that shows outstanding performance in Computer Vision, Natural Language Processing, and Artificial Intelligence. In particular, recently proposed ResNet architecture and its modifications produce state-of-the-art results in image classification problems. ResNet and mos…

Develops CLDS models to model neural activity with nonlinear dynamics.

The paper proves the existence of a tubular neighborhood for Finsler submanifolds.

The paper explores MMPR to select diverse models for scientific insight.

We treat the problem of estimation of orientation parameters whose values are invariant to transformations from a spherical symmetry group. Previous work has shown that any such group-invariant distribution must satisfy a restricted finite mixture representation, which allows the orientation parameter to be estimated u…

Large sample size brings the computation bottleneck for modern data analysis. Subsampling is one of efficient strategies to handle this problem. In previous studies, researchers make more fo- cus on subsampling with replacement (SSR) than on subsampling without replacement (SSWR). In this paper we investigate a kind of…

This paper considers statistical estimation problems where the probability distribution of the observed random variable is invariant with respect to actions of a finite topological group. It is shown that any such distribution must satisfy a restricted finite mixture representation. When specialized to the case of dist…

One important assumption underlying common classification models is the stationarity of the data. However, in real-world streaming applications, the data concept indicated by the joint distribution of feature and label is not stationary but drifting over time. Concept drift detection aims to detect such drifts and adap…

Bayesian optimization guided by experimenter intuition and beliefs.

New loss function handles uncertain constraints in CSLO problems.

This paper introduces the MCML approach for empirically studying the learnability of relational properties that can be expressed in the well-known software design language Alloy. A key novelty of MCML is quantification of the performance of and semantic differences among trained machine learning (ML) models, specifical…

Dual ML approach predicts peak temperatures in AFSD, improving process optimization.

Efficiently estimates material parameter space with multifidelity Gaussian process modeling.

New method selects variables for GP regression using sparse projection.