Results 71 to 80 of about 234,207 (337)

Phase Field Failure Modeling: Brittle‐Ductile Dual‐Phase Microstructures under Compressive Loading

Advanced Engineering Materials, EarlyView.
The approach by Amor and the approach by Miehe and Zhang for asymmetric damage behavior in the phase field method for fracture are compared regarding their fitness for microcrack‐based failure modeling. The comparison is performed for the case of a dual‐phase microstructure with a brittle and a ductile constituent.
Jakob Huber, Jan Torgersen, Ewald Werner
wiley   +1 more source

Accelerated Low-Rank Tensor Completion via Projected Tensor Block Coordinate Descent

IEEE Access
The low-rank tensor completion problem aims to find a low-rank approximation of a tensor by filling in missing entries from partially observed entries to enhance the accuracy of the tensor data analysis.
Geunseop Lee
doaj   +1 more source

Numerical Exploration of Thermal Shock Resistance in MgO–C Refractories

Advanced Engineering Materials, EarlyView.
A mesostructure‐resolved numerical framework is developed to evaluate the thermal shock resistance of MgO–C refractories. By modeling interface debonding under rapid temperature changes and introducing a modified thermal shock parameter that accounts for mesocracks, the study shows how graphite content and aggregate size influence thermal shock ...
Jishnu Vinayak Gopi, Stephan Roth, Christos G. Aneziris, Bjoern Kiefer   +3 more
wiley   +1 more source

Robust Tensor Decomposition for Heterogeneous Beamforming Under Imperfect Channel State Information

IEEE Open Journal of Signal Processing, 2023
We propose a new robust variation of the tensor decomposition known as the multi-linear generalized singular value decomposition (ML-GSVD), and demonstrate its effectiveness in the design of joint transmit (TX) and receive (RX) beamforming (BF) for both ...
Kengo Ando, Koji Ishibashi, Giuseppe Thadeu Freitas de Abreu   +2 more
doaj   +1 more source

Spectral Methods from Tensor Networks

, 2018
A tensor network is a diagram that specifies a way to "multiply" a collection of tensors together to produce another tensor (or matrix). Many existing algorithms for tensor problems (such as tensor decomposition and tensor PCA), although they are not ...
Anandkumar Animashree, Learning, Pita R. Gil   +2 more
core   +1 more source

Tensor decomposition and homotopy continuation [PDF]

, 2016
A computationally challenging classical elimination theory problem is to compute polynomials which vanish on the set of tensors of a given rank. By moving away from computing polynomials via elimination theory to computing pseudowitness sets via ...
Bernardi, Alessandra, Daleo, Noah S., Hauenstein, Jonathan D., Mourrain, Bernard   +3 more
core   +4 more sources

Decomposition loci of tensors

Journal of Symbolic Computation
25 pages.
Bernardi, A, Oneto, A, Santarsiero, P
openaire   +3 more sources

Toward Knowledge‐Based Workflows: A Semantic Approach to Atomistic Simulations for Mechanical and Thermodynamic Properties

Advanced Engineering Materials, EarlyView.
Knowledge‐based atomistic workflows are presented for mechanical and thermodynamic properties. By coupling modular simulations with ontology‐aligned metadata and provenance, Fe case studies on elastic behavior, defects, thermal properties, and Hall–Petch strengthening reveal how FAIR, queryable, and reusable simulation data can be generated. Mechanical
Abril Azócar Guzmán, Hoang‐Thien Luu, Sarath Menon, Tilmann Hickel, Nina Merkert, Stefan Sandfeld   +5 more
wiley   +1 more source

On the average condition number of tensor rank decompositions

, 2019
We compute the expected value of powers of the geometric condition number of random tensor rank decompositions. It is shown in particular that the expected value of the condition number of $n_1\times n_2 \times 2$ tensors with a random rank-$r ...
Breiding, Paul, Vannieuwenhoven, Nick
core   +1 more source

Bayesian inference of vector autoregressions with tensor decompositions [PDF]

, 2022
Yiyong Luo, Jim E. Griffin
openalex   +1 more source