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Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments.

, , , , , and . Reliab. Eng. Syst. Saf., (2021)

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Group testing case identification with biomarker information., , , and . Comput. Stat. Data Anal., (2018)Uncertainty quantification for Multiphase-CFD simulations of bubbly flows: a machine learning-based Bayesian approach supported by high-resolution experiments., , , , , and . Reliab. Eng. Syst. Saf., (2021)microASR: 32-μW Real-Time Automatic Speech Recognition Chip featuring a Bio-Inspired Neuron Model and Digital SRAM-based Compute-In-Memory Hardware., , , , , and . ESSCIRC, page 421-424. IEEE, (2023)A 177 TOPS/W, Capacitor-based In-Memory Computing SRAM Macro with Stepwise-Charging/Discharging DACs and Sparsity-Optimized Bitcells for 4-Bit Deep Convolutional Neural Networks., , , , , , , , , and . CICC, page 1-2. IEEE, (2022)Always-On, Sub-300-nW, Event-Driven Spiking Neural Network based on Spike-Driven Clock-Generation and Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device., , , , , , , , and . A-SSCC, page 1-4. IEEE, (2020)DIMCA: An Area-Efficient Digital In-Memory Computing Macro Featuring Approximate Arithmetic Hardware in 28 nm., , , , , , and . IEEE J. Solid State Circuits, 59 (3): 960-971 (March 2024)A Blockchain-Based Efficient and Verifiable Attribute-Based Proxy Re-Encryption Cloud Sharing Scheme., , and . Inf., 14 (5): 281 (May 2023)Parametric component detection and variable selection in varying-coefficient partially linear models., and . J. Multivar. Anal., (2012)Algorithm-Hardware Co-design for Ultra-Low-Power Machine Learning and Neuromorphic Computing. Columbia University, USA, (2023)DIMC: 2219TOPS/W 2569F2/b Digital In-Memory Computing Macro in 28nm Based on Approximate Arithmetic Hardware., , , , , and . ISSCC, page 266-268. IEEE, (2022)