Results 111 to 120 of about 19,027 (302)

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Sparse Polynomial Interpolation and Testing

, 2016
Interpolation is the process of learning an unknown polynomial f from some set of its evaluations. We consider the interpolation of a sparse polynomial, i.e., where f is comprised of a small, bounded number of terms.
Arnold, Andrew
core  

Data‐Driven High‐Throughput Volume Fraction Estimation From X‐Ray Diffraction Patterns

Advanced Intelligent Discovery, EarlyView.
Long exposure times and the need for manual evaluation limit the use of X‐ray diffraction in high‐throughput applications. This study presents a data‐driven approach addressing both issues. HiVE (a method for High‐throughput Volume fraction Estimation) performs composition estimation for high‐noise XRD patterns produced using polychromatic emission ...
Hawo H. Höfer, André Orth, Robert Wang, Ben Breitung, Simon Schweidler, Jasmin Aghassi‐Hagmann, Markus Reischl   +6 more
wiley   +1 more source

Interpolation algorithm for computing Drazin inverse of polynomial matrices

, 2007
In this paper we introduce an interpolation method for computing the Drazin inverse of a given polynomial matrix. This method is an extension of the known method from [A. Schuster, P.
Petković, Marko D., Stanimirović, Predrag S.   +1 more
core   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Dimension reduction of thermoelectric properties using barycentric polynomial interpolation at Chebyshev nodes. [PDF]

Sci Rep, 2020
Chung J, Ryu B, Park S.
europepmc   +1 more source

Umbral Interpolation: A Survey

A survey on recent umbral polynomial interpolation is presented. Some new results are given, following a matrix-determinant approach.
Francesco Aldo Costabile, Anna Napoli, Maria Italia Gualtieri   +2 more
core   +1 more source

On Multivariate Polynomial Interpolation

, 1989
A class of spaces of multivariate polynomials, closed under differentiation, is studied and corresponding classes of well posed Hermite-type interpolation problems are presented.
Dyn, Nira, Ron, Amos, N. Dyn, A. Ron
core   +1 more source

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

Combined Shepard operators with Chebyshev nodes

Journal of Numerical Analysis and Approximation Theory, 2004
In this paper we study combined Shepard-Lagrange univariate interpolation operator\[S_{n,\mu}^{L,m}(Y;f,x):=S_{n,\mu}^{L,m}(f,x)=\frac{\sum\limits_{k=0}^{n+1}\left\vert x-y_{n,k}\right\vert ^{-\mu}(L_{m}f)(x,y_{n,k})}{\sum\limits_{k=0}^{n+1}\left\vert x ...
Cristina O. Oşan, Radu T. Trîmbitaş
doaj   +2 more sources