Conferences  >  Mathematics  >  Modeling and Simulation

ICMS - International Centre for Mathematical Sciences

This workshop will focus on developing new statistical solutions to address these new ecological data-driven challenges.

Applied Mathematics (in general),    Probability and Statistics, Game Theory

The Fields Institute

Differential equations, in particular evolutionary differential equations, are used to model, often with uncanny effectiveness, a vast number of natural and social phenomena. The study of dynamical behaviors of the underlying models, e.g. their stabilities, robustness, bifurcations, and complexities, has become truly essential to understand the phenomena being modeled. It is precisely because of the modeling effectiveness of evolutionary differential equations that their dynamical study is among the fastest growing and most active multidisciplinary research fields today. Not only is the existing theory of dynamics of evolutionary equations closely intertwined with many areas of mathematics, but also the concepts, methods, and paradigms, introduced in the study have become an indispensible component of a multitude of works of more applied nature, in disciplines including chemistry, physics, material science, mechanics, electrical engineering, as well as in the emerging applications of social sciences, not to mention the explosion during the last several decades of applications related to the biological and life sciences. As we push the boundaries of applicability of the existing theory, we routinely discover that new mathematical and computational tools are needed, often symbiotically. To witness, it is widely accepted that an important factor contributing to the explosive growth in studying dynamics of evolutionary differential equations has been the availability of sophisticated and powerful computational tools. Yet, huge systems, systems with multiple time scales, multi-physics models, stochastic differential equations, non-smooth evolutionary problems, still transcend our collective understanding, and exhibit inherent complexities which are requiring the development of sophisticated new modeling and numerical techniques, and whose validity will need to be validated by rigorous mathematics.

Calculus, Differential Equations and Integration,    Analysis

Isaac Newton Institute for Mathematical Sciences (INI)

Imperial College London

The objective of the conference is to provide a forum for exchange of ideas and discussions on topics related to different forms of numerical modelling in geotechnical engineering. Both senior and young researchers, scientists and practitioners from Europe and overseas are warmly invited to attend this conference to share and exchange their knowledge.

Civil Engineering, Architecture, Construction and Geotechnical Engineering,    Geophysics and Geology

This 14th Edition aims to cover traditional topics of MCM conferences (Computational statistical physics, Quasi Monte Carlo methods, Markov Chain Monte Carlo in high dimension, Multilevel methods, Rare events simulation, Stochastic gradient and other stochastic optimization methods�) as well as emerging topics coming from applications and from artificial intelligence as: Generative models, Experimental design in Uncertainty Quantification, Monte Carlo simulations and High Performance computing, Reinforcement learning and control, Statistical learning and Monte Carlo sampling, Economic and industrial applications (Deeptech, finance, medicine, climate risks for instance).

Email: https://mcm2023.sciencesconf.org/

XX edition of the School is devoted to the challenges of “Mathematical and Computational Modelling Across the Scales”pertaining to physical problems involving micro-, meso-, macro- and multi-scale phenomena. The thematic focus is broad, encompassing contributions on modelling and numerical methods, for direct and inverse problems, with applications spanning from wave propagation to composite materials and complex fluids, from quantum mechanics to biological and building simulations.

Institute of Mathematics and its Applications

The 12th International Conference on Modelling in Industrial Maintenance and Reliability (MIMAR) will take place in Nottingham, UK from 4-6 July 2023. This event is the premier maintenance and reliability modelling conference in the UK and builds upon a very successful series of previous conferences. It is an excellent international forum for disseminating information on the state-of-the-art research, theories and practices in maintenance and reliability modelling and offers a platform for connecting researchers and practitioners from around the world.

Conference Team;     Email: conferences@ima.org.uk

Engineering Economy and Cost Analysis • Life cycle/performance analysis, Maintenance and Reliability Modelling, Prognostics and Health Management, Reliability and Maintenance Engineering, Safety, Security and Risk Management, Spare Parts Supply Chain Management, Warranty Management and Data Analysis, Machine learning for reliability engineering and maintenance optimization

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

In this workshop, we will explore how the research realms of generative modelling and 3D neural representations can benefit from each other, as well as explore novel applications in different areas of science and technology.

Neural Networks and Artificial Intelligence, Machine Learning,    Scientific Computing and Data Science

Isaac Newton Institute for Mathematical Sciences (INI)

Courses and Events for Math Students and Early Career Researchers,    Courses and Events for Physics Students

Isaac Newton Institute for Mathematical Sciences (INI)

This summer school will provide a thorough introduction to the material standing at the core of the USM programme. Lectures will cover state-of-the-art techniques and methodologies for stochastic modeling, uncertainty propagation and inverse problems, as well as emerging methods for adaptivity and probabilistic learning. A broad array of applications will be presented, ranging from UQ-informed materials design to the stochastic modeling of complex, multiscale physical systems. The workshop is suited in particular for PhD students, postdocs and academic researchers from computational engineering, materials science, mathematics, and statistics.

Courses and Events for Math Students and Early Career Researchers,    Courses and Events for Physics Students

Isaac Newton Institute for Mathematical Sciences (INI)

The aim of this summer school is to foster the development of a community of quantitative scientists who, while working on different application areas, share their knowledge and help address fundamental questions in the mathematics of movement. The goal is to identify common challenges and cross-fertilise concepts and techniques for modelling and analysing movement data in animal ecology and cell biology. With basic, as well as advanced, topics on the mathematics of movement the summer school welcomes early career researchers who that are new novice to the area but also those who that are already familiar with it. The first three days will be dedicated, respectively, to (i) Data Acquisition and Technology, (ii) Data Analysis, and (iii) Modelling, while the 4th and 5th days will be spent working in separate groups on specific dataset and modelling challenges. There will be sessions for early career presentations and poster sessions, as well as opportunities to hear about successful interdisciplinary collaborations and various activities to enable networking with the programme organisers and the many academic participants of the programme. There will also be a competition for the best poster.

Systems Biology and Computational Biology,    Courses and Events for Math Students and Early Career Researchers

The International Association for Mathematical Geosciences is a international scientific multidisciplinary society with about 700 – 800 members in over 65 countries. The mission of the IAMG is to promote worldwide the advancement of mathematics, statistics and informatics in the geosciences.

Mining geostatistics and geometallurgy, Meshing, Machine learning, Non-linear regression, Optimization, Petroleum geostatistics, Tectonics, Uncertainty modelling, Geochemistry, Flow in porous media, Resource potential estimation and prospectivity mapping, CODA, Geodynamics, Analysis of spatio-temporal data, 3D (geometric) modelling, Computational petrology, Geophysics and inversion, Geologic forward modeling

Applied Mathematics (in general),    Geophysics and Geology

Isaac Newton Institute for Mathematical Sciences (INI)

Modelling collective group movement in animal ecology and cell biology has, over the years, followed two relatively parallel modelling paths. Most prevalent for the former are what one might define as discrete level models, whereby computational constructs are used to keep track of the spatio-temporal dynamics of each individual in a group. Problems in the latter, cell biology, have been tackled mostly by continuous models whereby the details of the individuals are neglected, in favour of a description of the entire population through, e.g. a density function. However, more recently hybrid agent-based/population models have gained increasing usage.

As the overall aim of the workshop is to create an overarching modelling framework with which to interpret common empirical features, the exchange of ideas and techniques from the different communities is an invaluable opportunity to move forward and at the same to delineate the boundaries of applicability of the different approaches in collective movement research.

Applied Mathematics (in general),    Systems Biology and Computational Biology

The Fields Institute

This workshop will showcase advances related to the Fields thematic program on geometric constraint systems, framework rigidity and distance geometry. Particular topics of interest include matrix and tensor product completions, kinematics and flexibility, polynomial constraint systems, structured polynomials including sums of squares, matrix factorizations, Euclidean distance minimization and maximum likelihood estimation.

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Computational Physics and Numerical Simulation,    Mathematical Physics

Isaac Newton Institute for Mathematical Sciences (INI)

The study of interactions among living entities covers broad scientific domains, ranging from molecules and cells through individual organisms and groups to species, populations and communities. Common to all domains is the need to quantify the presumed interactions at the relevant spatial and temporal scales. While no formal theory exists in the literature, there exist different approaches that aim to construct a mechanistic representation of how individuals interact with each other as well as how to measure such interactions, but in most cases such approaches are specific to the problem at hand. The common factor among the existing approaches is their intention to represent the sequential nature of how organisms interact, first acquiring information from their surroundings, then processing that information and subsequently selecting a movement response.

By cross-fertilising different ideas and techniques in animal ecology and cell biology we aim to gain a more realistic perspective on measurement and representation of organism interactions, revisiting existing concepts, and permitting new discoveries and insights.

Applied Mathematics (in general),    Systems Biology and Computational Biology

The Third Conference of Young Applied Mathematicians in Siena is organized by PhD students and postdocs from University of Pavia, University of Siena, and University of Bath in the topics of Applied Mathematics. In particular, the conference targets Master's students, PhD students, and postdocs interested in the fields of Numerical Modelling, Numerical Analysis, Machine Learning and Statistics.

Numerical Analysis and Computational Mathematics,    Complex Networks

Isaac Newton Institute for Mathematical Sciences (INI)

Applied Mathematics (in general),    Systems Biology and Computational Biology

Image-Based Simulation for Industry (IBSim-4i) is organised by the Zienkiewicz Centre for Computational Engineering, Swansea University and the Institute of Physics (IOP) with support from EPSRC and CCPi Tomographic Imaging.

Image-based meshing is the process by which 3D images (e.g., X-ray CT or laser scanning) are converted into ultra-high-resolution simulations. 3D imaging is increasingly being used in the industrial sector for inspection, non-destructive testing / evaluation (NDT/NDE) and metrology but image-based simulation is still an underutilised technique. Our aim is that the activities of IBSim-4i will facilitate a wider adoption of image-based simulation and provide a platform to discuss the cutting-edge developments in the field.

Pattern Recognition and Image Processing, Machine Vision,    Metrology and Instrumentation

Centre de recherches mathématiques (CRM)

While brain function originates in neuron, its energy is supported by the vascular system. With the recent development of anatomically detailed synthetic network models of the entire cerebral circulation, it is now possible to perform detailed simulations of physiology in realistic brains of small animals and humans on the computer. Modeling microcirculatory blood flow as a biphasic suspension of red-blood-cell and plasma, blood pressure, flow and hematocrit can be simulated in whole brains on the computer. Combining Poiseuille’s hemodynamic simulations with advection/diffusion equations describing oxygen diffusion, the human brain metabolism can now be modeled in-silico, by integrating tissue oxygen consumption and differential equations describing compartments associated with neural and glial brain cells. In parallel, energy metabolism regulation in the brain and its modeling has greatly progressed over the past few decades. This workshop will thus be focused on digital human brains, and their use to predict critical metabolic functions namely blood flow, oxygen extraction and cellular metabolism across all length scales down to the level of individual capillaries and cells. Courses and conferences will be combined with research presentations and practical workshops.

Applied Mathematics (in general),    Systems Biology and Computational Biology

Isaac Newton Institute for Mathematical Sciences (INI)

The purpose of this workshop is to bring together scientists interested in different areas of complex movement in a biological context, from field research, to data analysis, to theoretical modelling. In particular, the focus will be on complex movement beyond the Markov property, such as memory-dependent animal foraging processes, spatial avoidance due to past deposition of animal scent or resource depletion, and directed motion in neutrophils. Despite its ubiquity, the mathematical difficulty in dealing with history dependence has hampered the study of non-Markov movement processes. Past empirical limitations are also responsible for the lack of a general theory to describe history dependent movement processes, as detection of features that would distinguish between Markov and non-Markov model predictions were not attainable. Currently, movement data may reach unprecedented resolution, and there is a renewed interest in developing non-Markov movement models, exemplified by recent theoretical studies to devise general prescriptions to simulate non-Markov stochastic processes.

Applied Mathematics (in general),    Systems Biology and Computational Biology

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

What have we learned from the COVID-19 pandemic, and how can we be better prepared for the next global outbreak? This workshop brings together collaborative teams of modellers, statisticians, epidemiologists, genomics experts, public health decision-makers, and those implementing and delivering interventions who have been working together in a research network, aiming to increase Canada’s capacity for data-driven emerging infectious disease modelling to directly support future public health decisions. This BIRS meeting is an important opportunity for network members and collaborators to share the outcomes of their research over the two years since the network was launched with funding from the federal government. The questions being tackled at this workshop are grounded in public health needs and generated in partnership between research investigators and knowledge users – public health leaders, health administrators and policy-makers.

Epidemiology & Public Health,    Systems Biology and Computational Biology

CIRM – Centre International de Rencontres Mathématiques

Computational Physics and Numerical Simulation,    Scientific Computing and Data Science

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Probability and Statistics, Game Theory,    Complex Systems, Chaos and Self-Organisation

Fields Institute

This workshop aims to showcase the state-of-the-art mathematical and computational models that are in use in mathematical oncology, with a particular focus on the fourteen hallmarks of cancer. New modeling challenges arise from these hallmarks. Some of the hallmarks relate to the characteristics of the cancer cells themselves (sub-cellular and cellular level), while others include more global effects such as blood circulation, the immune response, the effect and role of the microenvironment, mechanical forces, and the microbiome. Matching the biological diversity of the cancer hallmarks, is the diversity of mathematical approaches and techniques that have been used and are in use to give insight into the growth and spread of cancer – for example, continuum and discrete models; ordinary and partial differential equation models; individual-based and agent-based models; hybrid models; deterministic and stochastic approaches; analytical and computational approaches. Many of these processes act on very different spatial and temporal scales. Hence a new integrated multiscale mathematical modeling framework is needed to include new aspects and insights, such as spatial stochastic models and local and non-local partial differential equation models.

Oncology,    Systems Biology and Computational Biology

Fields Institute

This workshop aims to take a closer look at how mathematical and computational models can help answer clinically relevant questions and enhance multimodal treatments. Currently, a wide range of modelling approaches study cancer and have a crucial role in treatment delivery from answering questions related to cancer biology and treatment efficacy to directly helping with treatment delivery. In this workshop, we would like to bring together clinicians, biomedical experts, experimentalists, and mathematicians to explore some of the relevant questions on multimodal treatment modelling, efficacy, optimization, drug resistance and clinical delivery.

Oncology,    Systems Biology and Computational Biology

Fields Institute

This workshop explores research in methods aimed at connecting mathematical models of cancer evolution and clinical data. It focuses on ecology and evolution of cancer. Traditionally, ecology and evolution have been the most “mathematical” (and perhaps the first mathematical) areas of biology. Mathematical contributions to these fields made in the 20th century are now fundamental parts of scientific knowledge. More recently, methods of ecology and evolution have found their way into oncology, bridging traditional molecular biology and mathematics including stochastic processes, dynamical systems, and nonlinear partial differential equations. New biological challenges lead to difficult mathematical problems and beautiful solutions, which are both innovative mathematically and impactful from the point of view of public health. These will be the focus of the proposed workshop .

Oncology,    Systems Biology and Computational Biology

Fields Institute

Immunotherapies have revolutionized treatments for several types of cancers, but have been met with clinical trial failures in others. The idea of eliciting an immune response against a tumour is old, but we are only just beginning to harness the potential of the immune system to treat both solid and liquid tumours. To improve immunotherapeutic approaches requires understanding how the immune system interacts around and within a tumour, allowing us to establish effective immunotherapeutic protocols. Mathematical modelling can help identify the mechanisms at the heart of immunotherapeutic efficacy and design successful therapeutic regimens. Close collaboration between experimentalists, clinicians, and quantitative researchers in academia and industry is required to forward models for implementation. This workshop will focus on tightening the links between researchers in different fields to fuel immunotherapy success.

Oncology,    Systems Biology and Computational Biology