NUMSTAT 2026 invites original research contributions in numerical methods, statistical computing, scientific machine learning, and their applications in engineering and allied disciplines. The topics listed under each track are indicative and not exhaustive.
Numerical methods for ordinary and partial differential equations
Finite difference, finite element, and finite volume methods
Spectral, meshfree, and boundary element methods
Numerical linear algebra and iterative solvers
Adaptive and multiscale computational methods
Numerical optimisation and inverse problems
Stability, convergence, and error analysis
Parallel and high-performance scientific computing
Computational methods for integral and integro-differential equations
Development and analysis of scientific computing algorithms
Incompressible and compressible fluid flows
Laminar, transitional, and turbulent flows
Natural, forced, and mixed convection
Heat and mass transfer
Multiphase and reactive flows
Flow and heat transfer in porous media
Conjugate heat transfer and fluid–structure interaction
Phase-change and thermal-energy-storage systems
Microfluidics and microscale heat transfer
Computational modelling of energy and thermal systems
Multiphysics simulations and coupled-field problems
Verification and validation of computational models
Linear and nonlinear stability analysis
Temporal and spatial stability
Thermal and buoyancy-driven instabilities
Convective and absolute instabilities
Transition from laminar to turbulent flow
Stability of multiphase and porous-media flows
Eigenvalue and bifurcation analysis
Computational solid and structural mechanics
Nonlinear mechanics and large-deformation analysis
Fracture, fatigue, and damage mechanics
Contact and impact mechanics
Composite, smart, and functionally graded materials
Computational materials science
Multiscale modelling of materials and structures
Structural optimisation and topology optimisation
Computational modelling of manufacturing processes
Additive manufacturing, machining, forming, and casting
Process simulation and manufacturing optimisation
Mechanics of advanced and sustainable materials
Statistical modelling and computational statistics
Bayesian computation and statistical inference
Monte Carlo and Markov chain Monte Carlo methods
Experimental design and sampling techniques
Regression, multivariate, and time-series analysis
Spatial and spatio-temporal statistics
Agricultural experiments and field-trial analysis
Crop, soil, livestock, and environmental data analysis
Statistical methods for precision agriculture
Uncertainty propagation and sensitivity analysis
Stochastic modelling and probabilistic methods
Reliability analysis and risk assessment
Survival analysis and extreme-value modelling
Statistical quality control and process monitoring
Physics-informed neural networks
Neural operators and machine-learning methods for differential equations
Data-driven modelling of engineering systems
Hybrid physics-based and data-driven models
Surrogate modelling and response-surface methods
System identification and parameter estimation
Machine learning for inverse problems
Reduced-data and sparse-data learning
Uncertainty-aware and explainable artificial intelligence
Predictive maintenance and fault diagnosis
Data-driven control and optimisation
Machine learning applications in fluid mechanics, heat transfer, mechanics, materials, and manufacturing
Trustworthy and interpretable AI for engineering applications
Reduced-basis and reduced-order modelling methods
Proper orthogonal decomposition and dynamic mode decomposition
Projection-based and data-driven model reduction
Real-time simulation and control
Digital twins for engineering systems
Sensor integration, data assimilation, and state estimation
Condition monitoring, diagnostics, and prognostics
Lifecycle modelling and predictive maintenance
Smart manufacturing and intelligent infrastructure
Sustainable design and resource optimisation
Renewable-energy and energy-efficient systems
Low-carbon and environmentally responsible engineering
Digital twins for energy, manufacturing, transportation, and infrastructure
Reduced-order models for multiphysics and large-scale systems
Submissions addressing emerging topics or interdisciplinary applications related to the broader scope of NUMSTAT 2026 are also welcome.
Authors are invited to submit a one-page extended abstract using the prescribed NUMSTAT 2026 template.
(The online submission portal will be available soon. For submission-related queries, contact: numstat2026@iiitm.ac.in)
