Physics simulators are essential in science and engineering, enabling the analysis, control, and design of complex systems. In experimental sciences, they are increasingly used to automate experimental design, often via combinatorial search and optimization. However, as the setups grow more complex, the computational cost of traditional, CPU-based simulators becomes a major limitation. Here, we show how neural surrogate models can significantly reduce reliance on such slow simulators while preserving accuracy. Taking the design of interferometric gravitational wave detectors as a representative example, we train a neural network to surrogate the gravitational wave physics simulator Finesse, which was developed by the LIGO community. Despite that small changes in physical parameters can change the output by orders of magnitudes, the model rapidly predicts the quality and feasibility of candidate designs, allowing an efficient exploration of large design spaces. Our algorithm loops between training the surrogate, inverse designing new experiments, and verifying their properties with the slow simulator for further training. Assisted by auto-differentiation and GPU parallelism, our method proposes high-quality experiments much faster than direct optimization. Solutions that our algorithm finds within hours outperform designs that take five days for the optimizer to reach. Though shown in the context of gravitational wave detectors, our framework is broadly applicable to other domains where simulator bottlenecks hinder optimization and discovery.

Space-based gravitational wave detection is one of the most anticipated gravitational wave (GW) detection projects in the next decade, which is promising to detect abundant compact binary systems. However, the precise prediction of space GW waveforms remains unexplored. To solve the data processing difficulty in the increasing waveform complexity caused by detectors' response and second-generation time-delay interferometry (TDI 2.0), an interpretable pre-trained large model named CBS-GPT (Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer) is proposed. For compact binary system waveforms, three models were trained to predict the waveforms of massive black hole binary (MBHB), extreme mass-ratio inspirals (EMRIs), and galactic binary (GB), achieving prediction accuracies of 99%, 91%, and 99%, respectively at most.The CBS-GPT model exhibits notable generalization and interpretability, with its hidden parameters effectively capturing the intricate information of waveforms, even with complex instrument response and a wide parameter range. Our research demonstrates the potential of large pre-trained models in gravitational wave realm, opening up new opportunities and guidance for future researches such as the complex waveforms generation, gap completion, and deep learning model design for GW science.

The detection of gravitational waves, an accomplishment that earned the Nobel Prize in physics last fall, has revolutionized astronomy. Despite all the excitement about the phenomenon, however, American gravitational wave detectors have spotted them just six times to date. Scientists would very much like to have more data to work with, and they're turning to artificial intelligence to try to identify more gravitational wave signals faster, Wired reported. That's because gravitational wave detectors are most valuable when they work together with other types of instruments to shed light on what's happening in the universe. In order for that to happen, it's not just about the detectors picking up a signal--scientists also have to realize it's there soon enough to enlist colleagues in the investigation.