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.


Important Dates


Previous IPDO Symposia