Uncertainty-aware inverse modeling for federated scientific discovery across DOE facilities
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Schotthoefer, Steffen [ORNL] (ORCID:00000002156965↗
Engineering topics
Archibald, Rick [ORNL] (ORCID:0000000245389780)
Publications and source records attributed to Archibald, Rick [ORNL] (ORCID:0000000245389780).
Tuning the Interpolation Basis in a Multigrid Decomposition for Local Error Control
In the compression of scientific data, error-controlled compressors enable to considerably decrease the size of the dataset while maintaining adequate levels of accuracy. In this paper, we note that multi-level refactoring scheme such as MGARD i) rely on an approximation of the data based on the interpolation of coefficients, ii) estimate the resulting error with global metrics on the dataset. To improve on these two aspects, we propose a method that aims to divide the original dataset into blocks based on their smoothness and refactors each block separately with the most relevant interpolation order. We show the relevance of such a method on tailored datasets and the benefits and challenges when applying it to large scientific data.
Vidal, Nicolas [ORNL]↗
A Framework for Compressing Unstructured Scientific Data via Serialization
We present a general framework for compressing unstructured scientific data with known local connectivity. A common application is simulation data defined on arbitrary finite element meshes. The framework employs a greedy topology preserving reordering of original nodes which allows for seamless integration into existing data processing pipelines. This reordering process depends solely on mesh connectivity and can be performed offline for optimal efficiency. However, the algorithm’s greedy nature also supports on-the-fly implementation. The proposed method is compatible with any compression algorithm that leverages spatial correlations within the data. The effectiveness of this approach is demonstrated on a large-scale real dataset using several compression methods, including MGARD, SZ, and ZFP.
Reshniak, Viktor [ORNL] (ORCID:0000000315454462)↗
Privacy Preserving Federated Learning for Advanced Scientific Ecosystems
We present a framework to provide privacy preserving (PP) federating learning (FL) across multiple computational and experimental facilities. This work joins the compute capabilities of National Energy Research Scientific Computing Center (NERSC) and Oak Ridge National Laboratory Research Cloud (ORC) with simulated experimental data, such as those produced at the SLAC National Accelerator Laboratory and Spallation Neutron Source (SNS). We describe the software infrastructure developed to provide privacy for computational and experimental networks. We developed algorithmic privacy across the federated system by embedding database security, computation, and communication into the federation architecture, utilizing scientific tools developed by the experimental community.
Archibald, Rick [ORNL] (ORCID:0000000245389780)↗