Mathematics for Industry Network

Mathematics for Industry Network
Name	Mathematics for Industry Network
Formation	2000s
Type	Research network
Region	International
Headquarters	Europe

Mathematics for Industry Network is an international consortium linking applied mathematicians, industry engineers, and technology firms to solve practical problems through mathematical modelling and computation. The network promotes collaboration among academic institutions, corporations, and research laboratories to translate theoretical advances into industrial applications, drawing participants from universities, national laboratories, and private companies across Europe, North America, and Asia. It emphasizes cross-disciplinary engagement between academic departments, corporate research centers, and public research institutes.

Overview

The network connects researchers from institutions such as University of Cambridge, University of Oxford, Massachusetts Institute of Technology, Stanford University, and ETH Zurich with industry partners including Siemens, IBM, General Electric, Shell plc, and BASF. Membership often includes scientists from national bodies like CERN, European Space Agency, Los Alamos National Laboratory, Lawrence Berkeley National Laboratory, and Centre National de la Recherche Scientifique. Participants collaborate on problems associated with companies such as Airbus, Boeing, Toyota, Schlumberger, and Philips and work alongside funding agencies like European Research Council, National Science Foundation, and Deutsche Forschungsgemeinschaft.

History and Development

Origins trace to collaborative initiatives inspired by programs at institutions like INRIA, Max Planck Society, KTH Royal Institute of Technology, Rutherford Appleton Laboratory, and meetings influenced by conferences at Imperial College London and Princeton University. Early workshops echoed themes from gatherings organized by Society for Industrial and Applied Mathematics, International Mathematical Union, and project grants involving Wellcome Trust and Horizon 2020. The network expanded through alliances with industrial consortia such as European Technology Platform efforts and bilateral projects with corporations like BP and Rolls-Royce.

Structure and Membership

Governance typically mirrors committees found at Royal Society, Academia Europaea, National Academy of Sciences, and American Mathematical Society, with advisory boards including representatives from Royal Society of Edinburgh and European Science Foundation. Membership comprises faculty from departments at University of California, Berkeley, Princeton University, Harvard University, University of Tokyo, and Peking University, researchers from laboratories such as Sandia National Laboratories and Argonne National Laboratory, and industrial scientists from firms like Microsoft Research and Google DeepMind. Collaborative nodes often form around centers such as Oxford Centre for Collaborative Applied Mathematics and programs similar to MIT Industrial Liaison Program.

Activities and Programs

Typical programs include problem-driven challenges modeled after initiatives by DARPA, translational research projects comparable to EPSRC centers, and training akin to summer schools held at Collège de France and Mathematical Sciences Research Institute. Projects address case studies from companies including ABB, Bosch, TotalEnergies, Schneider Electric, and Valeo. The network organizes joint supervision arrangements reminiscent of partnerships between ETH Zurich and ETH Zurich's Industry Partners, postdoctoral schemes modeled on Marie Skłodowska-Curie Actions, and collaborative grants with agencies like European Investment Bank.

Research Themes and Impact

Research spans applied topics practiced at institutions such as Caltech and Johns Hopkins University: computational fluid dynamics problems relevant to NASA, optimization challenges studied at INSEAD collaborations with McKinsey & Company, data-driven modeling echoing projects at Facebook AI Research, and uncertainty quantification akin to work at Sandia National Laboratories. Case studies include predictive maintenance methods used by Siemens Energy, materials modelling informed by MIT Materials Research Laboratory, image reconstruction techniques similar to those at Philips Research, and algorithmic trading strategies paralleling approaches at Goldman Sachs. Outcomes have influenced standards and products at Nokia, Ericsson, Honeywell, and 3M.

Conferences, Workshops, and Training

The network convenes events modeled after conferences such as International Congress of Mathematicians, workshops resembling Newton Institute programs, and industry days comparable to SIGGRAPH and NeurIPS industrial tracks. Training initiatives mirror formats from Centre for Mathematical Sciences, Cambridge summer schools, doctoral consortiums like those at European Mathematical Society, and executive short courses similar to offerings by Wharton School or INSEAD Executive Education.

Partnerships and Industry Engagement

Partnerships include collaborations with research councils such as Engineering and Physical Sciences Research Council, collaborations with industrial research labs like Honda Research Institute, and joint ventures with public research organizations such as Fraunhofer Society and TNO. Engagement mechanisms draw on models used by Bell Labs, Toyota Research Institute, Bureau of Labor Statistics consultancy, and corporate-academic collaborations seen between Sony and university partners. The network often negotiates intellectual property arrangements comparable to those at Imperial Innovations and participates in technology transfer analogous to Cambridge Enterprise.

Category:Applied mathematics organizations