Results 41 to 50 of about 1,525 (223)

Automated generative process synthesis via transformer‐based dual‐loop simulation and optimization

open access: yesAIChE Journal, EarlyView.
Abstract This study presents a novel framework for automated generative process synthesis, addressing the complexity of simultaneously optimizing discrete topologies and continuous operating variables. To overcome conventional superstructure limitations, we propose a dual‐loop architecture integrating generative transformers with rigorous process ...
Yeong Woo Son   +4 more
wiley   +1 more source

Theorems on the zeros of linear differential operators

open access: yesИзвестия высших учебных заведений. Поволжский регион: Физико-математические науки
Background. Differential connections between solutions of systems of differential equations play a significant role in mathematics and mathematical physics. The operators and algebras of differential symmetry of linear homogeneous systems of differential
A.I. Fomin, V.I. Titarenko
doaj   +1 more source

Topological dual of non-locally convex Orlicz-Bochner spaces [PDF]

open access: yes, 1999
summary:Let $L^\varphi (X)$ be an Orlicz-Bochner space defined by an Orlicz function $\varphi $ taking only finite values (not necessarily convex) over a $\sigma $-finite atomless measure space. It is proved that the topological dual $L^\varphi (X)^*$ of
Nowak, Marian, Marian Nowak
core  

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

open access: yesAdvanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob   +2 more
wiley   +1 more source

Supporting weakly Pareto optimal allocations in infinite dimensional nonconvex economies [PDF]

open access: yes
In this paper, we prove a new version of the Second Welfare Theorem for economies with a finite number of agents and an infinite number of commodities, when the preference correspondences are not convex-valued and/or when the total production set is not ...
Monique Florenzano   +2 more
core  

The fixed point index for noncompact mappings in non locally convex topological vector spaces [PDF]

open access: yes, 1994
summary:We introduce the relative fixed point index for a class of noncompact operators on special subsets of non locally convex ...
Hahn, Siegfried   +2 more
core  

On the fixed point index in locally convex spaces

open access: yes, 1987
SynopsisLet E be a Hausdorff locally convex space, Q a convex closed subset of E and U an open subset of Q. We develop an index theory for a class of locally compact maps f: U → E for which the usual assumption f(U) ⊂ Q is replaced by an appropriate ...
M. P. Pera, M. Furi
core   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

open access: yesAdvanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang   +4 more
wiley   +1 more source

Production equilibria [PDF]

open access: yes
This paper studies production economies in a commodity space that is an ordered locally convex space. We establish a general theorem on the existence of equilibrium without requiring that the commodity space or its dual be a vector lattice.
Monique Florenzano   +2 more
core  

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Home - About - Disclaimer - Privacy