The toolkit’s greatest potential lies in its ability to complement or replace computationally intensive simulations with lightweight, AI-driven surrogate models. Testing ideas on a surrogate rather than an expensive simulation could radically alter the way we do science. Surrogates are deep-learning models that take data from a previous simulation to predict how altering a few variables will influence the outcome of the experiment. Surrogates allow you to test one idea after another at a fraction of the cost. A trained deep-learning surrogate may not be as precise as a physical simulation, but the results are good enough to rapidly validate or reject a hypothesis. ST4SD is unique in providing a single environment for scientists to run simulations and surrogates alike, seamlessly integrating these two modes of experimentation. Our discovery pipeline lets you move around simulation or surrogate tasks like Lego bricks, recycling completed tasks whenever possible. Through a technique called memoization, the pipeline automatically identifies duplicate tasks in real time and swaps them out for the already executed version, boosting productivity.