Final exam — integrated assessment

Part 11 — Self-assessment quizzes

Learning objectives

Test integrated understanding across all 11 parts of the textbook
Prepare for industry interviews or PhD-qualifier-style exams
Identify cross-topic gaps in recall

The final exam draws 20 questions at random from the full ~110-question bank spanning Parts 0–10. Rerun for a fresh draw. Passing threshold for interview readiness: 80%+ (16 / 20). The exam takes ≈30–45 minutes with rationale reading.

Topics span: neural-network foundations (Part 0), PINN formulation (Part 1), architectures and training (Parts 2-3), wave-equation physics (Part 4), forward modelling (Part 5), velocity / travel-time inversion (Parts 6-7), operator learning (Part 8), hybrid + UQ (Part 9), and real-field capstones (Part 10).

References

Raissi, M., Perdikaris, P., Karniadakis, G.E. (2019). Physics-informed neural networks. J. Comput. Phys. 378, 686–707.
Karniadakis, G.E., Kevrekidis, I.G., Lu, L., Perdikaris, P., Wang, S., Yang, L. (2021). Physics-informed machine learning. Nat. Rev. Phys. 3, 422–440.
Cuomo, S., Di Cola, V.S., Giampaolo, F., et al. (2022). Scientific machine learning through physics-informed neural networks: Where we are and what is next. J. Sci. Comput. 92(3), 88.
Rasht-Behesht, M., Huber, C., Shukla, K., Karniadakis, G.E. (2022). Physics-informed neural networks (PINNs) for wave propagation and full waveform inversions. JGR Solid Earth 127, e2021JB023120.
Virieux, J., Operto, S. (2009). An overview of full-waveform inversion in exploration geophysics. Geophysics 74(6), WCC1–WCC26.