The Physics-Informed Machine Learning Society connects engineering knowledge, mathematical models and machine intelligence through research, education and collaboration.
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One-Day FDP: 25 September 2026
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Annual Meeting: 08–09 July 2027
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Bengaluru, India
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pimlsociety@gmail.com
Physics-Informed Machine Learning Society
Advancing Engineering Through Machine Intelligence. Join an international professional community connecting engineering, physical sciences, mathematics and artificial intelligence through Physics-Informed Machine Learning (PIML), Physics-Informed Neural Networks (PINNs), scientific machine learning, research, education and collaboration.
Physics-Informed Machine Learning Across 265 Branches
Ten broad families keep this page easy to navigate. Each family opens a complete list where every engineering branch has its own individual PIML information.
Mechanical, Mechatronics & Manufacturing
Fluid mechanics, heat transfer, solid mechanics, manufacturing, robotics, predictive maintenance and digital twins.
View 31 Branches
Civil, Construction & Built Environment
Structures, geotechnical systems, hydrology, transportation, materials, smart infrastructure and structural health monitoring.
View 28 Branches
Chemical, Process & Pharmaceutical
Reaction kinetics, transport phenomena, process modelling, multiphase flow, process control and engineering surrogates.
View 31 Branches
Electrical, Electronics & Communication
Power systems, smart grids, circuits, renewable energy, electromagnetics, communication systems, sensors and fault diagnosis.
View 51 Branches
Computing, AI & Information Technology
PINNs, neural operators, scientific computing, optimisation, digital twins, robotics and trustworthy scientific AI.
View 41 BranchesBiomedical, Biotechnology, Agriculture & Food
Bioprocesses, cell culture, systems biology, biosensors, biomedical systems and knowledge-informed biological modelling.
View 26 BranchesMaterials, Metallurgy, Mining & Ceramics
Alloy design, thermodynamics, phase transformations, microstructures, materials processing and metallurgical digital twins.
View 12 BranchesAerospace, Automotive, Mobility & Marine
Aerodynamics, propulsion, vehicle and vessel dynamics, structures, condition monitoring and transport digital twins.
View 14 BranchesEnergy, Safety & Climate Technologies
Energy-system modelling, renewables, nuclear systems, fire and risk prediction, resilient operation and climate-aware design.
View 9 BranchesTextile, Fashion, Leather, Printing & Packaging
Material-process modelling, quality prediction, sustainable production, apparel, printing and packaging optimisation.
View 22 BranchesYour exact branch is included, even when it is not shown as a homepage card. Search all 265 engineering branches.
Building an Interdisciplinary Community for Physics-Informed AI
Physics-Informed Machine Learning combines artificial intelligence with physical laws, governing equations and scientific constraints. PIMLS brings domain engineers and scientists together with computer-science and AI researchers to develop reliable, meaningful and responsible solutions for real engineering systems.
- Interdisciplinary research communities
- Faculty development and hands-on training
- Research collaboration across engineering branches
- Conferences, lectures and technical workshops
- Responsible and scientifically grounded machine intelligence
Founding year 2026
Founding Year
The Society is currently in its founding and community-development phase.
First Annual Meeting
PIAIEA 2027 will host the first annual meeting on 08–09 July 2027.
FDP Sessions
Four morning sessions and two afternoon hands-on laboratory sessions.
Annual Membership
Annual individual membership is ₹1,500 for one year.
Learn, Collaborate and Help Shape PIMLS
Explore the Society's first faculty development program, flagship annual meeting and official logo design initiative.
PIMLS Logo Design Competition
Students, researchers and designers are invited to help create a professional visual identity representing engineering, physical sciences, mathematics and machine intelligence.
One-Day Faculty Development Program
Physics-Informed Machine Learning for Engineering Applications on 25 September 2026. The program introduces PIML foundations, governing equations, PINNs, engineering applications and practical implementation.
PIAIEA 2027 — First Annual Meeting
The international conference will bring together researchers, academicians, industry professionals and students working in PIML, PINNs and AI for engineering applications. The organizing committee will be updated soon.
Interested in participating, hosting a program or proposing a research activity? Contact the PIMLS coordinator
Our Community
Research Communities
Engineering Applications
Scientific Machine Learning
Our Community
Our Community
Research Communities
Engineering Applications
Scientific Machine Learning
Our Community
Research, Education and Professional Community
PIMLS is being developed around continuing research interaction, educational programs, scholarly communication and interdisciplinary participation.
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Computing Technology Research Journal
The official journal of PIMLS publishes interdisciplinary work in computing technologies, PIML, Scientific Machine Learning, computational modelling and engineering-driven artificial intelligence.
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ISSN: 2583-6501
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Frequency: Quarterly
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Open Access • Online
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Be Part of the PIML Society
Join faculty members, researchers, professionals, scholars and students interested in combining machine learning with physical laws, mathematical models and engineering knowledge.
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Annual Fee: ₹1,500
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Validity: One Year
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Interdisciplinary Community
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Propose a Research Collaboration
Researchers may contact PIMLS about engineering applications, interdisciplinary projects, workshops, institutional programs and participation in emerging research communities.
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All Engineering Branches
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Faculty • Researchers • Students
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Bengaluru, India
Membership connects researchers with interdisciplinary learning, mentoring, technical activities and opportunities to participate in the growing PIMLS community.
Current Academic Support for PIMLS
PIMLS is in its founding phase. Permanent office bearers will be decided through the appropriate Society process after the First Annual Meeting.
Dr. Preetam Ghosh
International Academic Advisory Support
Professor of Computer Science, Virginia Commonwealth University, USA.
Dr. Rogerio Atem de Carvalho
International Academic Advisory Support
Full Professor of Informatics, Instituto Federal Fluminense, Brazil
Dr. Dushyant Kumar Singh
International Academic Advisory Support
Associate Professor, Dept. of CSE, MNNIT, Uttar Pradesh
Dr. Mehran Mazandarani
International Academic Advisory Support
Director of AI Technology Development, CFIT Co. Ltd., China.
Dr Prakash Kota
International Academic Advisory Support
Adjunct Professor, Economics Department, Massry School of Business, University at Albany, New York, USA and Founder & CEO, MLPowersAI, Inc., USA
Siva Kiran RR
Interim Coordinator, PIMLS • Chief Editor, CTRJ
Coordinates founding activities, research enquiries and academic communication for the emerging Society.
Clear answers to your questions
Still have a question?
Contact the interim coordinator for membership, research collaboration, FDP participation or Society-related enquiries.
Physics-Informed Machine Learning integrates data-driven learning with governing equations, physical laws, mathematical relationships or scientific constraints. It helps models learn while remaining connected to the behaviour of real engineering systems.
Faculty members, researchers, scientists, industry professionals, research scholars and students from engineering, computer science, mathematics, physical sciences, biotechnology and allied disciplines are welcome.
Applicants complete the membership form and email their current biodata to PIMLS. Applications are reviewed, and approved applicants receive the next steps for annual membership.
PIMLS is currently in its founding phase. The permanent governance structure and inaugural office bearers are expected to be discussed and constituted through the appropriate Society process after the First Annual Meeting in July 2027.