Big Data in Experimental Mechanics and Model Order Reduction: Today’s Challenges and Tomorrow’s Opportunities

摘要: Since the turn of century experimental solid mechanics has undergone major changes with generalized use images. The number acquired data literally exploded and one today’s challenges is related to saturation mining procedures through such big sets. With respect digital image/volume correlation tomorrow’s pathways better control master this flow that are optimized for extracting sought information minimum uncertainties maximum robustness. In paper emphasis put on various hierarchical identification procedures. Based structures a posteriori model/data reductions performed in order ease make exploitation far more efficient. Some possibilities other model reduction techniques like proper decomposition discussed new opportunities sketched.