Results 181 to 190 of about 276,249 (270)

Frequency‐dependent contraction rates for the Bayesian method to the inverse source problem

Transactions of the London Mathematical Society, Volume 13, Issue 1, December 2026.
Abstract This paper addresses an inverse source problem for acoustic waves in a range of frequencies. Our study has two main goals. First, although the problem is severely ill‐posed with a logarithmic stability estimate, we demonstrate, through careful analysis of the forward map's singular values, that increasing the frequency range enhances stability,
Pu‐Zhao Kow, Jenn‐Nan Wang
wiley   +1 more source

Fault Tree Reliability Analysis via Squarefree Polynomials: Mathematical and Experimental Analysis. [PDF]

SN Comput Sci
Lopuhaä-Zwakenberg M.
europepmc   +1 more source

Novel Bernstein‐Kantorovich Type Operators

Mathematical Methods in the Applied Sciences, Volume 49, Issue 16, Page 17069-17095, 15 November 2026.
ABSTRACT In this paper, we introduce a novel class of Bernstein‐Kantorovich operators, denoted as K β n , θ $$ {K}_{\beta}^{n,\theta } $$ , parameterized by 0 < β < 1 $$ 0<\beta <1 $$ and θ > 0 $$ \theta >0 $$ . We first derive a recurrence formula that facilitates the computation of moments and central moments and provide explicit expressions for K β ...
Pembe Sabancigil, Nazim I. Mahmudov
wiley   +1 more source

The Category of Anyon Sectors for Non-Abelian Quantum Double Models. [PDF]

Commun Math Phys
Bols A, Hamdan M, Naaijkens P, Vadnerkar S.   +3 more
europepmc   +1 more source

An Euler system for GU(2, 1). [PDF]

Math Ann, 2022
Loeffler D, Skinner C, Zerbes SL.
europepmc   +1 more source

From Hyperelastic Physics‐Augmented Neural Networks to Fortran User Materials: Standalone Implementation and Computational Performance in Explicit Dynamics

International Journal for Numerical Methods in Engineering, Volume 127, Issue 21, 15 November 2026.
ABSTRACT Data‐driven material modeling techniques have gained significant attention due to their ability to capture the complex constitutive behavior beyond the limitations of classical material models. Physics‐augmented neural networks (PANNs), which incorporate physical restrictions into their architecture, combine the flexibility of machine learning
L. Maurer, S. Eisenträger, D. Juhre, M. Bulla   +3 more
wiley   +1 more source

Fusions of Consciousness. [PDF]

Entropy (Basel), 2023
Hoffman DD, Prakash C, Prentner R.
europepmc   +1 more source

Stable Cuts, NAC‐Colourings and Flexible Realisations of Graphs

Journal of Graph Theory, Volume 113, Issue 3, Page 411-427, November 2026.
ABSTRACT A (2‐dimensional) realisation of a graph G is a pair ( G , p ), where p maps the vertices of G to R 2. A realisation is flexible if it can be continuously deformed while keeping the edge lengths fixed, and rigid otherwise. We say that G is rigid if every generic realisation of G is rigid; otherwise, G is flexible. In this paper, we investigate
Katie Clinch, Dániel Garamvölgyi, John Haslegrave, Tony Huynh, Jan Legerský, Anthony Nixon   +5 more
wiley   +1 more source

Thermodynamics à la Souriau on Kähler Non-Compact Symmetric Spaces for Cartan Neural Networks. [PDF]

Entropy (Basel)
Fré PG, Sorin AS, Trigiante M.
europepmc   +1 more source