Physics-Informed Machine Learning Society
FDP: 25 September 2026
Annual Meeting: 08–09 July 2027
Andhra Pradesh, India
pimlsociety@gmail.com
Physics-Informed Machine Learning across engineering, science and emerging technologies.
Fluid mechanics, heat transfer, solid mechanics, manufacturing, dynamics, robotics, predictive maintenance and engineering digital twins.
Example directions: PINNs for heat conduction, neural operators for flow prediction and physics-guided fault diagnosis.
Explore Mechanical & PIMLStructural mechanics, geotechnical systems, earthquake engineering, hydrology, transportation, construction materials and structural health monitoring.
Explore Civil & PIMLReaction kinetics, transport phenomena, process modelling, multiphase flow, process control, energy systems and chemical-process digital twins.
Explore Chemical & PIMLPower systems, smart grids, electrical machines, renewable energy, batteries, circuit models, fault diagnosis and energy optimisation.
Explore EEE & PIMLAlloy design, thermodynamics, phase transformations, heat treatment, microstructures, corrosion, processing and digital twins.
Explore Metallurgy & PIMLPhysics-Informed Neural Networks, neural operators, Scientific Machine Learning, differentiable programming, automatic differentiation, optimisation and trustworthy AI.
Explore CSE & PIMLBiological-system modelling, biomechanics, physiological signals, medical imaging, drug transport, computational biology and biomedical digital twins.
Explore Biotechnology & PIMLExplore the complete directory of 265 supplied engineering branches, organised into ten interdisciplinary families. Every listed branch opens its own PIML information page.
Contact us about research collaboration, programs, membership or institutional participation.