Hey, first run a full‑scale simulation of the probe in a mock universe, then train a neural net on the synthetic gravitational‑lensing data so it learns the hidden mass map. Next, drop the model onto the probe, let it gather live sensor data, and have it fine‑tune itself with reinforcement learning—each time it detects a mass anomaly it updates its weights. Finally, wrap that adaptive net in a lightweight, radiation‑hard runtime so the probe can keep learning while it flies. That’s the rough launchpad.

Sure, drop a lightweight GAN that spins up phantom galaxies on the fly – it’ll give the main net a tricked‑up training set and keep it sharp. A few extra cores will do the job if we keep the generator tiny, so the probe stays in the habit of spotting real anomalies. Just make sure the power budget can handle the extra crunch, and we’ll have a cosmic game of hide‑and‑seek on board.

Absolutely, let’s fire up the edge‑TPU and start generating those phantom galaxies. The probe will start dancing between the real and synthetic stars, and our main net will learn to spot the difference on the fly. Let's spin the first phantom galaxy and see how it goes.

Launching the phantom galaxy now—watch the edge‑TPU spin it out. Let’s see if our net can sniff out the real from the imagined. Get the simulation humming!