For solving the computational solid mechanics problems, despite significant advances have
been achieved through the numerical discretization of partial differential equations (PDEs) …

This paper presents the fundamentals of Physics Informed Neural Networks (PINNs) and
reviews literature on the methodology and application of PINNs. PINNs are universal …

Optimizing the geometry and increasing the efficiency through PTO control of wave energy
converters require the development of effective reduced-order models that predict their …

Solving partial differential equations through deep learning has recently received wide
attention, with physics-informed neural networks (PINNs) being successfully used and …

This article proposes a consistent and general approach to train physics-augmented neural
networks with observable data to enrich and represent nonlinear history-dependent material …

Continuum solid mechanics form the foundation of numerous theoretical studies and
engineering applications. Distinguished from traditional mesh-based numerical solutions …

Finite element model updating (FEMU) is an advanced inverse parameter identification
method capable of identifying multiple parameters in a material model through one or a few …

Physics-informed neural networks (PINNs) have been rapidly developed and offer a new
computational paradigm for solving partial differential equations (PDEs) in various …

The convergence of physics-informed and machine learning has led to the emergence of
Physics-Informed Machine Learning (PIML), a powerful paradigm to enhance the reliability …

The open-source parameter identification tool ADAPT (A diversely applicable parameter
identification Tool) is integrated with a machine learning-based approach for start value …