Results 61 to 70 of about 191,927 (272)
Hans Duistermaat’s contributions to Poisson geometry [PDF]
Bulletin of the Brazilian Mathematical Society, New Series, 2011 Hans Duistermaat was scheduled to lecture in the 2010 School on Poisson Geometry at IMPA, but passed away suddenly. This is a record of a talk I gave at the 2010 Conference on Poisson Geometry (the week after the School) to share some of my memories of him and to give a brief assessment of his impact on the subject.
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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
Holomorphic Poisson Manifolds and Holomorphic Lie Algebroids
, 2008 We study holomorphic Poisson manifolds and holomorphic Lie algebroids from the viewpoint of real Poisson geometry. We give a characterization of holomorphic Poisson structures in terms of the Poisson Nijenhuis structures of Magri-Morosi and describe a ...
C. Laurent-Gengoux +4 more
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Poisson Geometry of Monic Matrix Polynomials [PDF]
International Mathematics Research Notices, 2015 We study the Poisson geometry of the first congruence subgroup $G_1[[z^{-1}]]$ of the loop group $G[[z^{-1}]]$ endowed with the rational r-matrix Poisson structure for $G=GL_m$ and $SL_m$. We classify all the symplectic leaves on a certain ind-subvariety of $G_1[[z^{-1}]]$ in terms of Smith Normal Forms.
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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 +3 more
wiley +1 more source
Tailoring Functional Properties of Ti–Ni–Cu Shape Memory Alloy Thin Films for MEMS Actuators
Advanced Engineering Materials, EarlyView.A comprehensive study of critical parameters required to develop well‐performing Ti–Ni–Cu thin film shape memory alloy microactuators is provided. Materials science and device integration aspects are integrated by addressing structural and physical relationships using complementary characterization techniques as well as a practical fabrication solution
Elaheh Akbarnejad +6 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.Do not let thermal drift and instrument artifacts deceive high‐temperature nanoindentation results. We compare classical Oliver–Pharr and automatic image recognition analyses across steels and a Ni alloy to quantify these effects. Accounting for artifacts reveals systematic softening with temperature, while Cr and Ni additions boost resistance ...
Velislava Yonkova +2 more
wiley +1 more source
APPLICATION OF THE VORTEX-IN-CELL METHOD FOR THE SIMULATION OF TWO-DIMENSIONAL VISCOUS FLOW
TASK Quarterly, 1999 In the paper the vortex in cell method for the simulation of the viscous flow in a complex geometry was described. Vorticity field is approximated by the collection of the particles that carries the circulation.
HENRYK KUDELA, HENRYK KUDELA
doaj
Geometry of Manifolds and Applications
MathematicsThis editorial presents 24 research articles published in the Special Issue entitled Geometry of Manifolds and Applications of the MDPI Mathematics journal, which covers a wide range of topics from the geometry of (pseudo-)Riemannian manifolds and their ...
Adara M. Blaga
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Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
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