collaborators

6 papers

math.NA2026

Shape optimisation for adaptive -refinement: the one-dimensional case with residual based error estimators

Philip J. Herbert

We consider -refinement for the finite element discretisation of a Poisson problem. The goal of -refinement is to reposition the nodes of a computational mesh in order to bet…

math.NA2026

Finite element approximation of the enthalpy formulation for Stefan problems on evolving surfaces

Philip J. Herbert, Thomas Sales, Chandrasekhar Venkataraman

We propose, and analyse, a spatially discrete evolving surface finite element method for the approximation of the enthalpy formulation of the two-phase Stefan problem posed on an e…

math.AP2026

Small deformations of a near cylindrical tube for the Canham-Helfrich Energy with applications to biological membranes

Charles M. Elliott, Carsten Gräser, Philip J. Herbert

In this article we develop a quadratic energy which approximates the Canham-Helfrich energy for a tube-like surface with clamped boundary and area constraint. The energy is suited…

math.OC2026

Combining diffuse and sharp interface methods in shape optimisation

Philip J. Herbert, Michael Hinze, Christian Kahle

We develop a concept for the numerical treatment of shape optimization problems based on the combination of phase field and sharp interface methods. On the one hand, phase field me…

math.OC2026

Global convergence of -steepest descent for PDE constrained shape optimisation with semilinear elliptic equations in function space

Klaus Deckelnick, Philip J. Herbert, Michael Hinze

We prove global convergence in function space for the steepest descent method in shape optimisation with semilinear elliptic partial differential equations. Steepest descent is rea…

math.NA2025

The Stochastic Steepest Descent Method for Robust Optimization in Banach Spaces

Neil K. Chada, Philip J. Herbert

Stochastic gradient methods have been a popular and powerful choice of optimization methods, aimed at minimizing functions. Their advantage lies in the fact that that one approxima…