Rapid detachment of a rigid sphere adhered to a viscoelastic substrate: An upper bound model incorporating Maugis parameter and preload effects
Just published on JMPS
research
Kinetics of ferroelastic domain switching with and without back-switching events: A phase-field study
I am pleased to share our first open-access article of 2025! just published in #ActaMaterialia.
Leonardo's universal friction coefficient is found to be universal after all!
It turns out that friction coefficient 0.25 suggested as universal by Leonardo da Vinci more than 500 years ago has some universaility, as minimum friction coefficient for any granular material: it makes me proud as italian ;)
I guess it would be interesting to show this experimental result also theoretically or numerically, any interest?
The Second Law as a constraint and admitting the approximate nature of constitutive assumptions
A scheme for treating the Second Law of thermodynamics as a constraint and accounting for the approximate nature of constitutive assumptions in continuum thermomechanics is discussed. An unconstrained, concave, variational principle is designed for solving the resulting mathematical problem. Cases when the Second Law becomes an over-constraint on the mechanical model, as well as when it serves as a necessary constraint, are discussed.
A new analytical model for fibrillar viscoelastic adhesion using the Schapery or the Shrimali-Lopez-Pamies nucleation models
Hello: I would be interested in any comment about this preprint on fibrillar viscoelastic adhesion, originally devised by Schargott Popov and Gorb, where we use for the first time not only the Schapery model for nucleation of cracks, but also the Shrimali and Lopez Pamies, which leads to quite stronger enhancement of adhesion (the limit is the square of the Schapery one), and pull-off with no real prior propagation phase. Propagation with Schapery nucleation criterion is found to be qualitatively similar to the Schapery and Persson-Brener propagation theories, except that
1 x Master by Research / PhD Funded Student @ Swinburne
Postgraduate (Master by Research / PhD) under Swinburne-National Road Safety Action Grants Program (NRSAGP) funded by Australia’s Department of Infrastructure, Transport, Regional Development, Communications and the Arts
B-Splines, and ML approximants for PDE via duality
Variational formulation based on duality to solve partial differential equations: Use of B-splines and machine learning approximants
N. Sukumar Amit Acharya
Many partial differential equations (PDEs) such as Navier–Stokes equations in fluid mechanics, inelastic deformation in solids, and transient parabolic and hyperbolic equations do not have an exact, primal variational structure. Recently, a variational principle based on the dual (Lagrange multiplier) field was proposed. The essential idea in this approach is to treat the given PDE as constraints, and to invoke an arbitrarily chosen auxiliary potential with strong convexity properties to be optimized. This leads to requiring a convex dual functional to be minimized subject to Dirichlet boundary conditions on dual variables, with the guarantee that even PDEs that do not possess a variational structure in primal form can be solved via a variational principle. The vanishing of the first variation of the dual functional is, up to Dirichlet boundary conditions on dual fields, the weak form of the primal PDE problem with the dual-to-primal change of variables incorporated. We derive the dual weak form for the linear, one-dimensional, transient convection diffusion equation. A Galerkin discretization is used to obtain the discrete equations, with the trial and test functions chosen as linear combination of either RePU activation functions (shallow neural network) or B-spline basis functions; the corresponding stiffness matrix is symmetric. For transient problems, a space-time Galerkin implementation is used with tensor-product B-splines as approximating functions. Numerical results are presented for the steady-state and transient convection-diffusion equation, and transient heat conduction. The proposed method delivers sound accuracy for ODEs and PDEs and rates of convergence are established in the L2 norm and H1 seminorm for the steady-state convection-diffusion problem.
Article: Jensen–Shannon divergence based novel loss functions for Bayesian neural networks
Ponkrshnan Thiagarajan, Susanta Ghosh, "Jensen–Shannon divergence based novel loss functions for Bayesian neural networks", Neurocomputing, 2024, 129115, ISSN 0925-2312. https://doi.org/10.1016/j.neucom.2024.129115
Highlights
