Machine learning basics: capacity, overfitting and under fitting, hyper parameters and validation sets, bias & variance; PAC model; Rademacher complexity; growth function; VC- dimension; fundamental concepts of artificial neural networks; single layer perceptron classifier; multi-layer feed forward networks; single layer feed-back networks; associative memories; introductory concepts of reinforcement learning, Markhov decision process.

Introduction – approximate solutions to governing differential equations (GDE) – finite element formulations starting from GDE – finite element formulations based on stationarity of a functional – one-dimensional finite element analysis; shape functions, types of elements and applications – two and three-dimensional finite elements – numerical integration – applications to structural mechanics and fluid flow.

Review of basic equations of elasticity – state of stress at a point – analysis of strain, constitutive relations – generalized Hook’s law – formulation of boundary value problems – solution of 2D problems – energy methods in elasticity – bending, shear and torsion – thin walled beams – applications.

Lab practices on applied and advanced techniques of metal forming, welding, heat-treatment. Material characterization / metallography practice.

Discussions on the manufacturing practices for polymers, composites and ceramics (including 3D printing options)- Demonstrations/ Practical sessions at ISRO centres

Fusion welding processes: GTAW-concepts, physics of arc, flux assisted processes, forces, pulsed and continuous current modes, GMAW-concepts, types of metal transfer, pulsed and synergic MIG welding, shielding gases in arc welding processes, Selection of filler wire in welding, EBW: Concepts, types and applications. LBW: Physics of lasers, types of lasers, operation of laser welding setup, advantages and limitations, applications, hybrid welding. Solid state welding processes: Friction welding: Concepts, types and applications.