A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep-learning CNN automates Cu alloy grain size evaluation.
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…
Bayesian optimization identifies optimal alloy formulations.
Study improves materials discovery for high-entropy alloys using sparse linear models.
ML predicts alloy properties considering chemistry, processing, and data transformations.
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…
Method reveals dissimilarity in alloys' Curie temperatures.
Study uses AI and ML to predict and optimize corrosion resistance of aluminum alloys.
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…
HAL accelerates the generation of training sets for accurate interatomic potentials.
Model predicts EMF of Ni-Mn-Ga MSMA, improved with GRNN.
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…
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…
A novel ensemble classifier improves vibration-based quality monitoring accuracy.
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…
Proposes a method to handle missing inputs in Bayesian optimization.
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…
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…
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.
Self-supervised learning improves RUL prediction with limited data in fatigue damage prognosis.
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…
A RL approach optimizes metal AM process parameters for consistent melt pool depth.
Support vector regression (SVR) has been widely used to reduce the high computational cost of computer simulation. SVR assumes the input parameters have equal sample sizes, but unequal sample sizes are often encountered in engineering practices. To solve this issue, a new prediction approach based on SVR, namely as hig…
Proposes a framework to fuse heterogeneous data sources for better modeling.
New method finds graphene nanocrystals with reduced DFT calculations.
Curves can bound only finitely many developable surfaces.
The paper extends Cartan development to infinite dimensional Lie groups.
The paper studies singularities on parallels of tangent developable surfaces of frontal curves.
Survey on new Ricci flow techniques.
Survey of software developers' experience with Github Copilot tool.
We provide a condition for spatial curves which rules out the development of a type I singularity. The condition is that after the last time for which an inflection point develops, if the torsion is ever everywhere non-negative, the curve cannot develop a type I singularity.
PrototypeML simplifies neural network design and development.
Survey on DDVV-type inequalities, their history, and recent developments.
Study on insurance risk management and sustainable development.
This manuscript develops the theory of agglomerative clustering with Bregman divergences. Geometric smoothing techniques are developed to deal with degenerate clusters. To allow for cluster models based on exponential families with overcomplete representations, Bregman divergences are developed for nondifferentiable co…
We develop a method to describe laws of random surfaces using surface holonomy.
This paper reviews the checkered history of predictive distributions in statistics and discusses two developments, one from recent literature and the other new. The first development is bringing predictive distributions into machine learning, whose early development was so deeply influenced by two remarkable groups at …
Study shows 'Belt and Road' node cities boost digital finance in China.
Establishes existence of maximal globally hyperbolic development for Einstein equations.
We investigate the relationship among characteristic curves on developable surfaces. In case parameter curves coincide with these curves, we show that the base curve of a developable surface could be either a plane curve, a circular helix, a general helix or a slant helix.
Motivated by applications in architecture and design, we present a novel method for increasing the developability of a B-spline surface. We use the property that the Gauss image of a developable surface is 1-dimensional and can be locally well approximated by circles. This is cast into an algorithm for thinning the Gau…
This article presents a theoretical model for a dynamic system based on sustainable development. Due to the relatively absence of theoretical studies and practical issues in the area of sustainable development, Romania aspires to the principles of sustainable development. Based on the concept as a process in which econ…