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Areas of Interest

The IPDO-2019 Symposium will emphasize a broad range of deterministic, statistical, analytical, computational and experimental approaches, which can be applied to the solution of inverse, design and multi-disciplinary optimization problems. Contributions dealing with practical applications are encouraged, such as in mechanics, vehicle engineering, civil engineering, aeronautics, micro-electronics, bio-medicine, Imaging, transport and sensing of pollutants, materials design and processing, remote sensing, non-destructive evaluation, acceleration of single-objective and multi-objective optimization, meta-models for high-dimensional problems, uncertainty quantification, unsupervised deep learning algorithms, etc. The topics listed below give a general guideline for possible contributions:

» Inverse Problems in: Mechanics, Aeronautics, Vehicle engineering, Civil engineering, Material science, Damage detection, Fault diagnosis, Imaging, Heat and mass transfer, Acoustics, Imaging, Bio-medicine, Electromagnetism, Geophysics, Underground prospecting, Transport and sensing of pollutants, Nondestructive evaluation, Remote sensing, Learning theory, etc.

» Numerical Algorithms: Ill-posedness analysis and Regularization techniques, Semi-inverse problems and methods, Large-scaled inverse problems, Sensitivity analysis, Evolutionary algorithms, Geometric problems, Determination of boundary and initial conditions, Dynamic load identification, Model verification and validation (V&V).

» Uncertain Quantification: Statistical and probabilistic methods, Bayesian inverse problems, Non-probabilistic uncertain inverse methods, Uncertainty quantification, Inverse problems with uncertain models, Inverse uncertainty propagation.

» Multidisciplinary Design and Optimization: Design sensitivity analysis and global optimization, Shape and topology Optimization, Multidisciplinary and multi-objective optimization, Design under uncertainty, Meta-models for high-dimensional problems.

» Data-driven Based Algorithms: Data analysis, Signal and noise processing, Pattern recognition, Identification based on machine learning, Unsupervised deep learning algorithms, Data assimilation methods, Inverse methods based on Kalman filter.