
Characterizing harmful data sources is crucial in determining when to use low-fidelity data sources in surrogate modeling for industrial design problems.


coremsg

characterizing-harmful-data-sources-in-multi-fidelity-surrogate-modeling


Characterizing Harmful Data Sources in Multi-Fidelity Surrogate Modeling


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The author introduces a Stochastic Gradient Descent method to address scalability issues in dynamic network models, providing accurate results and uncovering hidden forces within networks.


stochastic-gradient-descent-based-inference-for-dynamic-network-models-with-attractors


Stochastic Gradient Descent-Based Inference for Dynamic Network Models with Attractors
