The automation of PDE-constrained optimisation and its applications
Simon W. Funke
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On the Performance and Convergence of PINNs for Problems in Linear Elasticity
ABSTRACT Physics‐informed neural networks (PINNs) have emerged as a promising approach for solving partial differential equations by embedding physical laws directly into the loss function. However, their performance characteristics for problems in computational mechanics remain insufficiently understood.
Dipraj Kadlag +3 more
wiley +1 more source
Phase-Change Silicone Elastomers for Tough, Soft Actuators. [PDF]
Lee YJ +8 more
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Two Scale FE‐FFT‐Based Modeling of Cancellous Bone
ABSTRACT Osteoporosis is characterized by a loss of volume percentage of cortical bone, which reduces the loading capacity of this organ and increases its likelihood for fractures. The disease has the highest prevalence of any bone disease worldwide, with a particularly high incidence among the elderly.
Mischa Blaszczyk +3 more
wiley +1 more source
Poroelasticity derived from the microstructure for intrinsically incompressible constituents. [PDF]
Penta R +3 more
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On MAP Estimates and Source Conditions for Drift Identification in SDEs
ABSTRACT We consider the inverse problem of identifying the drift in an stochastic differential equation (SDE) from n$n$ observations of its solution at M+1$M+1$ distinct time points. We derive a corresponding maximum a posteriori (MAP) estimate, we prove differentiability properties as well as a so‐called tangential cone condition for the forward ...
Daniel Tenbrinck +3 more
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Innovative Aboodh-based gractional analytical methods for nonlinear Burgers' partial differential equations. [PDF]
Iqbal N +6 more
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On Cahn–Hilliard Type Viscoelastoplastic Two‐Phase Flows
ABSTRACT This contribution deals with a model for viscoelastoplastic two‐phase flows of Cahn–Hilliard type. We present the modeling framework for the flow, the notion of a generalized solution, namely the so‐called dissipative solution, and the key ideas of the existence proof.
Fan Cheng +2 more
wiley +1 more source
QEKI: A Quantum-Classical Framework for Efficient Bayesian Inversion of PDEs. [PDF]
Yong J, Tang S.
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Quantitative Strong Maximum Principles for Nonlocal and Degenerate Elliptic PDEs
Revista, Zen, MATH, 10
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