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Project Details
Funding Scheme : General Research Fund
Project Number : 14302218
Project Title(English) : Mathematical studies of a phase field approach to shape optimization 
Project Title(Chinese) : 形狀優化相場法的數學研究 
Principal Investigator(English) : Prof Lam, Kei Fong 
Principal Investigator(Chinese) :  
Department : Department of Mathematics
Institution : Hong Kong Baptist University
E-mail Address : akflam@hkbu.edu.hk 
Tel :  
Co - Investigator(s) :
Prof Garcke, Harald
Panel : Physical Sciences
Subject Area : Mathematics
Exercise Year : 2018 / 19
Fund Approved : 456,452
Project Status : Completed
Completion Date : 30-6-2022
Project Objectives :
We shall develop a phase field approximation of an abstract shape optimization problem with a PDE constraint, and under specific settings we recover previous applications to drag minimization and structural topology optimization studied in the literature.
We shall analyze the phase field approximation of the PDE constraint, and identify a suitable notion of solutions. We shall provide analytical results concerning the existence of solutions to the PDE constraint and the existence of a minimizer (optimal shape) to the abstract optimization problem in Objective 1.
We shall use the results obtain in Objective 2 to derive optimality conditions (often in the form of a coupled system of nonlinear PDEs or inequalities) with the method of Lagrange multipliers. Then, we employ asymptotic analysis to connect the optimality conditions from the phase field approach and the classical formulation for shape optimization.
We shall apply the results obtained in Objectives 1, 2 and 3 to two specific inverse problems: inverse acoustic scattering and electrical impedance tomography. In both applications, we shall carry out a systematic analysis for minimizers and optimality conditions. Furthermore, we shall investigate the stability of optimal shapes with respect to changes in the data.
We shall numerically realize the optimal shapes for the problems studied in Objective 4 by solving the corresponding optimality conditions, and compare the results from the phase field approach to those obtained from the current state-of-the-art solution methods for these inverse problems.
Abstract as per original application
(English/Chinese):
In shape optimization problems, the aim is to find a class of shapes that optimizes some prescribed goals under a partial differential equation (PDE) constraint. One example is drag minimization, seeking the shape of an impermeable object exhibiting the least drag while flowing through a fluid. The classical formulation for such problems involves representing the unknown shape as a mathematical surface, and seeks to derive optimality conditions (equations/inequalities involving nonlinear PDEs). The optimal shapes can then be realized numerically by solving the optimality conditions. However, shape optimization problems are often severely ill-posed (as the existence of optimal shapes is not guaranteed in general) and can be computationally intensive (due to the requirement of a new approximation of the computational domain at every numerical iteration). We propose to use a phase field approach to overcome the above problems, and for drag minimization, it has several important mathematical and numerical advantages over the classical approach; namely the phase field method can be justified (in the sense that existence of optimal shapes can be proved rigorously) and in addition both the PDE constraint and the optimality conditions are solved on the same fixed domain throughout the optimization procedure. Furthermore, by sending a small parameter to zero, the optimality conditions from the classical formulation are recovered. Hence, one may view the phase field approach as a consistent approximation for shape optimization problems. The goal of this proposal is to develop phase field approximations to study nonlinear inverse problems that can be casted as shape optimization problems. In light of the mathematical advantages outlined above, we believe that the proposed phase field approach may offer novel and alternative resolution strategies for such problems. Firstly, we consider an abstract shape optimization problem with the aim of providing analytical results for the PDE constraint and establish the existence of optimal shapes, the derivation of optimality conditions, as well as the connection with the classical formulation. Then, we apply this theory to two important examples of inverse problems: inverse acoustic scattering and electrical impedance tomography. Numerical simulations will be carried out to compare the proposed phase field approach with current state-of-the-art methods, and successful investigations will serve to enhance the applicability of the phase field approach.
形狀優化問題的目標是找到一類在偏微分方程(PDE)約束下優化某些規定目標的形狀。一個例子是尋求在流體內表現出最小阻力的物體的形狀。針對這些問題的經典方法涉及將未知的形狀表示為數學表面,並尋求導出最優性條件(涉及非線性PDE的方程/不等式)。然後,通過求解最優性條件,可以在數值上實現最佳形狀。然而,形狀優化問題往往是嚴重不適(因為通常不能保證最優形狀的存在)並且可以是計算密集型的(由於在每次數值迭代時需要新的計算域)。我們建議使用相場模式方法來克服上述問題,並且對於拖曳最小化的形狀優化問題,它比傳統方法具有幾個重要的數學和數值優勢: 相場模式方法可以有進一步分析(可以嚴格證明最佳形狀的存在)並且另外通過優化過程在相同的計算域上解決PDE約束和最優性條件。此外,通過發送一個小參數為零,就獲得了經典方法的最優性條件。因此,可以將相場模式方法視為形狀優化問題的一致近似。本計畫提議的目標是開發相場模式近似以研究可以作為形狀優化問題的非線性反問題。鑑於上述的數學優勢,我們認為所提出的相場模式方法可以為這些問題提供新穎的解決方案策略。首先,我們考慮一個抽象的形狀優化問題,目的是為PDE約束提供分析結果,建立最優形狀的存在,最優性條件的推導,以及與經典方法的聯繫。然後,我們將這個理論應用於兩個重要的反問題:逆聲散射和電阻抗斷層成像。 將進行數值模擬以將所提出的相場模式方法與當前最先進的方法進行比較,並且成功的研究將有助於增強相場模式方法的適用性。
Realisation of objectives: As mentioned in the mid-term report, even though one half of Objectives 4 and 5 were achieved by other authors shortly after the commencement of the project, the PI and his collaborators studied a different inverse problem to avoid overlapping with existing works in the literature. Then, in the remaining period the PI applied the proposed methodology to the emerging topic of 3D printing. In these new applications, a systematic analysis for minimizers and optimality conditions were carried out (100% fulfillment of Objectives 1, 2 and 3), along with numerical results showcasing the advantages of the proposed approach for shape optimization problems. Our new investigations can be regarded as 50% fulfillment of Objectives 4 and 5 as stated in the original proposal.
Summary of objectives addressed:
Objectives Addressed Percentage achieved
1.We shall develop a phase field approximation of an abstract shape optimization problem with a PDE constraint, and under specific settings we recover previous applications to drag minimization and structural topology optimization studied in the literature.Yes100%
2.We shall analyze the phase field approximation of the PDE constraint, and identify a suitable notion of solutions. We shall provide analytical results concerning the existence of solutions to the PDE constraint and the existence of a minimizer (optimal shape) to the abstract optimization problem in Objective 1.Yes100%
3.We shall use the results obtain in Objective 2 to derive optimality conditions (often in the form of a coupled system of nonlinear PDEs or inequalities) with the method of Lagrange multipliers. Then, we employ asymptotic analysis to connect the optimality conditions from the phase field approach and the classical formulation for shape optimization.Yes100%
4.We shall apply the results obtained in Objectives 1, 2 and 3 to two specific inverse problems: inverse acoustic scattering and electrical impedance tomography. In both applications, we shall carry out a systematic analysis for minimizers and optimality conditions. Furthermore, we shall investigate the stability of optimal shapes with respect to changes in the data.Yes50%
5.We shall numerically realize the optimal shapes for the problems studied in Objective 4 by solving the corresponding optimality conditions, and compare the results from the phase field approach to those obtained from the current state-of-the-art solution methods for these inverse problems.Yes50%
Research Outcome
Major findings and research outcome: We proposed a phase field shape optimization approach to study two types of problems: (i) a geometric inverse problem involving quasilinear magnetostatic equations, (ii) design problems in 3D and 4D printing involving linear elasticity equations. In both topics, we recast these shape optimization problems in terms of PDE-constrained minimizer problems and performed a systematic analysis for minimizers and optimality conditions. Furthermore, we studied the so-called sharp interface limit that allows us to relate our phase field optimality conditions to optimality conditions of a corresponding shape optimization approach in a rigorous setting. Numerical simulations of the phase field optimality conditions also demonstrated some novel and interesting designs for the application to 3D printing. In particular, our research results are among the first to apply this phase field methodology to quasilinear Maxwell equations and the field of additive manufacturing. These were published in some of the best applied mathematics journals, such as Inverse Problems, Applied Mathematics & Optimization, and ESAIM: Control, Optimization and Calculus of Variations.
Potential for further development of the research
and the proposed course of action:
While the connection between the proposed phase field methodology and classical shape optimization has been clearly established, we have observed that the proposed methodology can also perform some degree of topology optimization. The connection between the two is less clear, and so it would be very interesting to mathematically investigate whether the optimality conditions from topology optimization problems can be obtained from the phase field approach. Another direction worthy of pursuit is the application to the emerging field of 3D printing. In one of our works we focused on creating designs that do not require support structures during construction, but one can equally cast a shape optimization problem that optimizes the shape of the support structures during printing. These topics would make very good PhD research projects.
Layman's Summary of
Completion Report:
Inverse problems involving geometry and design commonly appear in many areas of applications, and due to their importance, it is essential to develop mathematical approaches that can accurately and efficiently obtain a reasonable solution. In this project, we demonstrated that our proposed phase field methodology is able to tackle highly challenging design and detection problems appearing in 3D printing and magnetic tomography. In the former application we are able to develop some novel designs of 3D structures that change their shape in response to an external stimulus. Our theoretical investigations showed that the phase field approach is mathematically sound, and it can be related to other mathematical approaches in the literature studied previously. Based on our observations, it is possible to apply our phase field approach to other problems in shape and topology optimization.
Research Output
Peer-reviewed journal publication(s)
arising directly from this research project :
(* denotes the corresponding author)
Year of
Publication
Author(s) Title and Journal/Book Accessible from Institution Repository
2020 Kei Fong Lam, Irwin Yousept*  Consistency of a phase field regularisation for an inverse problem governed by a quasilinear Maxwell system.  No 
2023 Harald Garcke, Kei Fong Lam*, Robert Nurnberg, Andrea Signori  Overhang penalization in additive manufacturing via phase field structural topology optimization with anisotropic energies  No 
2023 Harald Garcke, Kei Fong Lam*, Robert Nurnberg and Andrea Signori  Phase field topology optimisation for 4D printing  No 
Recognized international conference(s)
in which paper(s) related to this research
project was/were delivered :
Month/Year/City Title Conference Name
Krakow Total variation and phase field regularisations of an inverse problem with quasilinear magnetostatic equations  International conference Dynamics, Equations and Applications (DEA2019) Section D44 
Wilmington, NC Phase field structural optimization in additive manufacturing  The 13th AIMS International Conference on Dynamical Systems, Differential Equations, and Applications 
Other impact
(e.g. award of patents or prizes,
collaboration with other research institutions,
technology transfer, etc.):

  SCREEN ID: SCRRM00542