Zheng Sun, Zihan Xu, Yang Yu, Shilin Xia, Breanna Tuhlei, Tengjun Man, Bo Zhou, Yuanhua Qin, Dong Shang
Effectiveness of an "online + in-person" hybrid model for an undergraduate molecular biology lab during COVID-19
Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology
rapid evolution sensor technology advances instrumentation progress devising dataacquisition software hardware providing vast amounts data various complex phenomena occur heterogeneous media ranging atmospheric environment largescale porous formations biological systems tremendous increase speed scientific computing also made possible emulate diverse multiscale multiphysics phenomena contain elements stochasticity heterogeneity generate large volumes numerical data thus given heterogeneous system annealed quenched disorder complex phenomenon occurs one analyze model system phenomenon explain data make predictions length time scales much larger data collected divide systems three distinct classes governing equations physical phenomena interest well data known solving equations large length scales long times difficult ii data available governing equations partially known sense either contain various coefficients must evaluated based data number degrees freedom system large deriving complete equations difficult impossible result one must develop governing equations reduced dimensionality iii third class systems large amounts data available governing equations phenomena interest known several classes physicsinformed datadriven approaches analyzing modeling three classes systems emerging based machine learning symbolic regression koopman operator morizwanzig projection operator formulation sparse identification nonlinear dynamics data assimilation combined neural network stochastic optimization analysis perspective describes methods latest developments highly important rapidly expanding area discusses possible future directions