Computational modelling of biological soft tissues 
by Thanyani A. Pandelania*, Dawood A. Desai†, Fulufhelo Nemavhola & and Harry M. Ngwangwab*
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* Department of Mechanical, Bioresources and Biomedical Engineering, School of Engineering and the Built Environment, College of Science, Engineering and Technology, University of South Africa, Private Bag X6, Florida, 1710, South Africa.
a. epandet@unisa.ac.za
b. ngwanhm@unisa.ac.za
https://www.unisa.ac.za/
†Tshwane University of Technology, Private Bag X680, Pretoria, 0001, South Africa
desaida@tut.ac.za
https://www.tut.ac.za/faculties/engineering/departments/mechanical/research-postgraduate-studies
&Department of Mechanical Engineering, Faculty of Engineering and the Built
Environment, Durban University of Technology,
P O Box 1334, Durban, 4000, South Africa.
FulufheloN1@dut.ac.za
https://www.dut.ac.za/

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ABSTRACT

Introduction:

Biological soft tissues are subjected to large deformations with negligible volume changes and show an anisotropic mechanical response due to their internal structure. There has been lots of experimental testing on biological tissues, but computational modelling is required to fully understand the response of this tissues under different loadings.

Summary:

The purpose of this minisymposium is to bring together researchers in the field of modeling and optimisation of soft tissues This minisymposium will presents work on a computational framework to capture and couple important mechanical, chemical and biological aspect of soft tissue.

This session focuses on:

Understanding the fundamentals of biomedical tissue and modelling and characterisation of soft tissue using computational models.

Invited papers:

Papers are invited that cover any aspect of computational of soft biological tissues. There will be opportunities for round table discussions on future research directions within this biomechanics field in computational modelling.

Bridging Physics and Data: AI Methods in Solid and Fluid Mechanics
by Laura de Lorenzis¹, Michael Kaliske², Jörg Schröder³, and Sebastian Skatulla⁴
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¹ ETH Zürich, Institute for Mechanical Systems, ldelorenzis@ethz.ch
² Technische Universität Dresden, Institut für Statik & Dynamik der Tragwerke, michael.kaliske@tu-dresden.de
³ Universität Duisburg-Essen, Institute of Mechanics, j.schroeder@uni-due.de
⁴ University of Cape Town, Department of Civil Engineering, sebastian.skatulla@uct.ac.za.za

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Key words: Artificial Intelligence (AI), Machine Learning (ML), Physics-Informed Neural Networks (PINNs), Data-Driven Modelling, Surrogate Modelling, Hybrid Physics–Data Approaches

ABSTRACT

Recent advances in artificial intelligence (AI) and machine learning (ML) have opened transformative opportunities for solving complex problems in solid and fluid mechanics. This special session aims to bring together researchers working at the intersection of computational mechanics and data science to discuss the latest trends, developments, and methodologies applying AI and ML to mechanical systems. The focus is on exploring how these technologies can enhance modelling accuracy, computational efficiency, and physical interpretability in problems governed by continuum mechanics.

Traditional numerical methods such as the finite element, finite volume, and boundary element methods have achieved remarkable success in simulating mechanical behaviour. However, they often face challenges when dealing with high-dimensional parameter spaces, multi-scale interactions, non-linear material responses, and real-time computation requirements. AI and ML methods – ranging from deep neural networks and Gaussian processes to reinforcement learning and graph-based learning – offer new capabilities for addressing these limitations. They enable data-driven discovery of constitutive laws, rapid surrogate modelling, adaptive mesh refinement, and automated parameter calibration, while also facilitating the integration of heterogeneous experimental and simulation data.

By providing a platform for interdisciplinary exchange between experts in mechanics, computer science, and applied mathematics, this special session seeks to outline the state of the art and identify future directions for intelligent computational mechanics. Topics include, but are not limited to: data-driven constitutive modelling, AI-enhanced multiscale simulations, ML-based turbulence and fracture modelling, uncertainty-aware learning frameworks, and AI methods for real-time prediction and control in solid and fluid mechanics.

The session particularly welcomes contributions that combine machine learning with physical modelling principles, such as physics-informed neural networks (PINNs), operator learning, hybrid reduced-order models, and the embedding of conservation laws and symmetries into learning architectures. These approaches aim to merge the interpretability and robustness of physics-based formulations with the flexibility and scalability of data-driven learning, thereby fostering improved generalization beyond training data.

Granular Materials and Bulk Handling
by Corné Coetzee*
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* Stellenbosch University
Department of Mechanical and Mechatronic Engineering
ccoetzee@sun.ac.za

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Key words:
Granular Materials, Bulk Materials, Discrete Element Modelling, Computational Methods, Calibration, Application, Validation

ABSTRACT

Efficient bulk material handling is crucial for sectors like mining, construction, agriculture, manufacturing, pharmaceutical and the food industry. Granular materials are complex and diverse in nature, ranging from fine powders to grains, soil, and even fibres. Computational modelling of these materials
is challenging.

The purpose of this minisymposium is to bring together researchers in the field of modelling of granular materials. This includes all granular materials, for example fine powders, larger particles such as agricultural grains and mining ores, fibres, pharmaceutical and food products, etc.

Papers are invited that cover any aspect of the computational modelling of granular materials:

1. The development and application of computational methods such as the discrete element method (DEM), the material point method (MPM), smooth particle hydrodynamics (SPH), computational fluid dynamics (CFD), multi-body dynamics (MBD), and coupled methods such as CFD-DEM and DEM-MBD.
2. Modelling and experimental techniques focussing on a single contact between two bodies, or the bulk behaviour of more particles.
3. Calibration of material input parameters, including experimental methods and modelling and optimisation techniques.
4. Validation examples, including industry scale and laboratory scale applications.
5. Examples from industry where computational modelling is used.