Numerical simulations are ubiquitous in science and engineering. Machine
learning for science investigates how artificial neural architectures can learn
from these simulations to speed up scientific discovery and engineering
processes. Most of these architectures are trained in a supervised manner. They
require tremendous amounts of data from simulations that are slow to generate
and memory greedy. In this article, we present our ongoing work to design a
training framework that alleviates those bottlenecks. It generates data in
parallel with the training process. Such simultaneity induces a bias in the
data available during the training. We present a strategy to mitigate this bias
with a memory buffer. We test our framework on the multi-parametric Lorenz's
attractor. We show the benefit of our framework compared to offline training
and the success of our data bias mitigation strategy to capture the complex
chaotic dynamics of the system