PMC:7796369 / 11117-11815 JSONTXT

{"project":"LitCovid-sentences","denotations":[{"id":"T86","span":{"begin":0,"end":109},"obj":"Sentence"},{"id":"T87","span":{"begin":110,"end":290},"obj":"Sentence"},{"id":"T88","span":{"begin":291,"end":343},"obj":"Sentence"},{"id":"T89","span":{"begin":344,"end":489},"obj":"Sentence"},{"id":"T90","span":{"begin":490,"end":616},"obj":"Sentence"},{"id":"T91","span":{"begin":617,"end":698},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Our neural network model is capable of prediction orders of magnitudes faster than the full simulation model. More precisely, predictions were on average computed in 0.027 s, with minimum and maximum times of 0.025 and 0.039 s, respectively, in a simulation of 1000 random input variations. Distribution of the results can be seen in Figure 7. Notice that the time scale we achieve here is on the order of milliseconds, whereas previously we required minutes to produce these same results. We achieve an improvement of over 3000× in terms of speed, largely due to the computation cost of simulating the entire event. Furthermore, we do all this locally as opposed to the need for cloud computation."}