July 29, 2018

Multiscale representation of hexahedral meshes & compression

Companion pages:
A full-scale geological grid structure is decomposed onto embedded wavelet-like scales while preserving the discontinuities, here geological faults (red), using a morphological 2D wavelet:
Geological grid structures and discontinuities preservation (red painted faults)
Categorical properties like rock types (sandstone, limestone, shale)  can be upscaled according to a dedicated non-linear decomposition called modelet (patent #20170344676: Method of exploitation of hydrocarbons of an underground formation by means of optimized scaling):


Hexahedral mesh categorical property: rock type

Continuous properties (saturation, porosity, permeability, temperature) can be homogenized with a 3D Haar wavelet:

Hexahedral mesh continuous property: porosity

The HexaShrink methodology described above is detailed in the recently submitted paper: 
With huge data acquisition progresses realized in the past decades and acquisition systems now able to produce high resolution point clouds, the digitization of physical terrains becomes increasingly more precise. Such extreme quantities of generated and modeled data greatly impact computational performances on many levels: storage media, memory requirements, transfer capability, and finally simulation interactivity, necessary to exploit this instance of big data. Efficient representations and storage are thus becoming "enabling technologies" in simulation science. We propose HexaShrink, an original decomposition scheme for structured hexahedral volume meshes. The latter are used for instance in biomedical engineering, materials science, or geosciences. HexaShrink provides a comprehensive framework allowing efficient mesh visualization and storage. Its exactly reversible multiresolution decomposition yields a hierarchy of meshes of increasing levels of details, in terms of either geometry, continuous or categorical properties of cells. Starting with an overview of volume meshes compression techniques, our contribution blends coherently different multiresolution wavelet schemes. It results in a global framework preserving discontinuities (faults) across scales, implemented as a fully reversible upscaling. Experimental results are provided on meshes of varying complexity. They emphasize the consistency of the proposed representation, in terms of visualization, attribute downsampling and distribution at different resolutions. Finally, HexaShrink yields gains in storage space when combined to lossless compression techniques.
And there is a patent associated to HexaShrink, Method of exploitation of hydrocarbons of an underground formation by means of optimized scaling:

Method of exploitation of hydrocarbons of an underground formation by means of optimized scaling


July 10, 2018

Bioinformatics & datascience: Internship & PhD on multi-omics data

An PhD position is still available on Graph-based learning from integrated multi-omics and multi-species data (genomic, transcriptomic, epigenetic) between IFP Energies nouvelles and CentraleSupélec/INRIA Saclay. All the information is gathered at this address.

Some information is duplicated below:
Micro-organisms are studied here for their application to bio-based chemistry from renewable sources. Such organisms are driven by their genome expression, with very diverse mechanisms acting at various biological scales, sensitive to external conditions (nutrients, environment). The irruption of novel high-throughput experimental technologies provides complementary omics data and, therefore, a better capability for understanding for the studied biological systems. Innovative analysis methods are required for such highly integrated data. Their handling increasingly require advanced bioinformatics, data science and optimization tools to provide insights into the multi-level regulation mechanisms (Editorial: Multi-omic data integration). The main objective of this subject is to offer an improved understanding of the different regulation levels in the cell (from model organisms to Trichoderma reesei strains). The underlying prediction task requires the normalization and the integration of heterogeneous biological data (genomic, transcriptomic and epigenetic) from different microorganisms. The path chosen is that of graph modelling and network optimization techniques, allowing the combination of different natures of data, with the incorporation of biological a priori (in the line of BRANE Cut and BRANE Clust algorithms). Learning models relating genomic and transcriptomic data to epigenomic traits could be associated to network inference, source separation and clustering techniques to achieve this aim. The methodology would inherit from a wealth of techniques developed over graphs for scattered data, social networks. Attention will also be paid to novel evaluation metrics, as their standardization remains a crucial stake in bioinformatics. A preliminary internship position (summer/fall 2018) is suggested before engaging the PhD program. Information at: http://www.laurent-duval.eu/lcd-2018-intern-phd-epigenetics-omics-graph-processing.html