Writing study: Fast training of accurate physics-informed neural networks without gradient descent
← ICLR 2026 oral writing corpus
Chinmay Datar, Taniya Kapoor, Abhishek Chandra, Qing Sun, Erik L. Bolager, Iryna Burak, Anna Veselovska, Massimo Fornasier, Felix Dietrich · Official ICLR 2026 oral record
Figure, table and section ledger
Editorial contribution type: Learning & optimization algorithms (primary) · Inference, algorithms & systems efficiency.
Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.
- Canonical PDF
- 60 physical pages
- Before references / appendix
- ≈ 10.29 page-equivalents
- Main figures / tables
- 7 / 2
- Attached appendix figures / tables
- 23 / 32
- Attached appendix span
- ≈ 41.89 page-equivalents
Section-by-section lengths and source pages
The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.
| Top-level heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1 INTRODUCTION | 1 | ≈ 1.67 pp. | 2.78% |
| 2 SOLVING TIME-DEPENDENT PDES USING FROZEN-PINNS | 3 | ≈ 2.33 pp. | 3.88% |
| 3 EMPIRICAL RESULTS | 5 | ≈ 3.23 pp. | 5.38% |
| 4 CONCLUSION | 8 | ≈ 2.31 pp. | 3.85% |
| ACKNOWLEDGMENTS | 11 | ≈ 0.18 pp. | 0.3% |
| REFERENCES | 11 | ≈ 7.81 pp. | 13.02% |
| APPENDIX | 19 | ≈ 2 pp. | 3.33% |
| A EXTENDED REVIEW OF RELATED WORK | 21 | ≈ 1.51 pp. | 2.52% |
| B SUPPLEMENTARY METHODOLOGICAL DETAILS ON PDE SOLVERS | 22 | ≈ 10.26 pp. | 17.1% |
| C SUPPLEMENTARY DETAILS ON NUMERICAL EXPERIMENTS | 32 | ≈ 28.13 pp. | 46.88% |
All detected figure and table locations
Main text
Attached appendix
- Figure 8 · p. 24
- Table 31 · p. 24
- Figure 9 · p. 26
- Figure 10 · p. 27
- Figure 11 · p. 32
- Figure 12 · p. 34
- Figure 13 · p. 36
- Figure 14 · p. 36
- Table 3 · p. 37
- Figure 15 · p. 37
- Table 4 · p. 38
- Table 5 · p. 38
- Table 6 · p. 39
- Table 7 · p. 39
- Table 8 · p. 39
- Table 9 · p. 39
- Table 10 · p. 40
- Figure 16 · p. 40
- Figure 17 · p. 40
- Table 11 · p. 41
- Table 1 · p. 41
- Table 12 · p. 41
- Table 13 · p. 42
- Figure 18 · p. 42
- Figure 19 · p. 43
- Figure 20 · p. 44
- Figure 21 · p. 45
- Table 14 · p. 45
- Table 15 · p. 46
- Table 16 · p. 46
- Table 17 · p. 46
- Figure 22 · p. 47
- Figure 23 · p. 47
- Table 18 · p. 47
- Table 21 · p. 47
- Figure 24 · p. 48
- Figure 25 · p. 49
- Figure 26 · p. 49
- Table 19 · p. 50
- Table 20 · p. 50
- Table 22 · p. 50
- Table 24 · p. 51
- Table 23 · p. 51
- Table 25 · p. 52
- Table 26 · p. 52
- Table 27 · p. 52
- Figure 27 · p. 53
- Figure 28 · p. 54
- Table 28 · p. 54
- Table 29 · p. 56
- Figure 29 · p. 57
- Figure 30 · p. 58
- Table 30 · p. 59
- Table 32 · p. 60
- Table 33 · p. 60
Argument and organization
- Two bottlenecks and space–time separation
Motivates fixed spatial features with evolving output coefficients.
- Constructing and solving the reduced system
Adds sampling, boundary handling, SVD compression and an explicit training algorithm.
- Experiments organized by PDE difficulty
Tests temporal frequency, shocks, geometry, chaos and dimension before extended per-problem recipes.
Problem, claim and contribution
The introduction names optimization difficulty and non-causal time treatment separately, then maps the proposed solver components to those obstacles.
Section 1, pp. 1–3 · Main paper
Argument and section order
Experiment headings describe stressors rather than simply naming datasets, helping readers understand why each PDE is included.
Sections 3.1–3.8, pp. 6–8 · Main paper
Figures, captions and visual explanation
Color-matched spatial bases and temporal coefficients explain the factorization; separate arrows assign initial, boundary and PDE constraints to their respective solvers.
Figure 1, p. 2 · Main paper
Tables, comparisons and evidence
The summary table retains mesh-based methods, missing runtimes, GPU markers and low/high precision divisions. This is more informative than a single speedup column.
Table 1, p. 9 · Main paper
Appendix and reproducibility
A detailed contents list leads to each equation, boundary condition, parameter sweep and error map; the high-dimensional discussion admits the simple analytical structure of its benchmark.
Appendix contents, pp. 19–20; Appendix C.8, pp. 54–60 · Main paper
Clarity and writing conventions
Some supplementary numerical ratios and transcribed derivations need reconciliation: Table 22's width and timing ratios do not match its displayed operands, and the wave rearrangement changes a sign.
Appendix B.2.3, p. 29; Table 22, p. 50 · Main paper
What to reuse—and what not to infer
Transferable lesson: Organize numerical experiments around the failure modes a method is intended to solve, and compare runtime only alongside explicit accuracy targets and conventional numerical baselines.
Boundary: Do not generalize smooth, known-PDE demonstrations to arbitrary high-dimensional problems or inverse imaging. Check printed equations and ratio arithmetic against executable code before reproducing them; the observed performance is not a universal guarantee.
Apply this lesson using the argument and evidence worksheet.
Inspection record and sources
- Checked
- 2026-09-19
- Reading status
- Full text + selected visuals inspected
- Main text
- Pages 1–10, Sections 1–4 and reproducibility/ethics statements
- Appendix / supplement
- Pages 19–60, contents and Appendices A–C.8: related work, all PDE-to-ODE derivations, boundary treatments, sampling discussion, every benchmark specification and ablation table
- Visually inspected PDF pages
- 2, 9
- ICLR 2026 final proceedings · 60 pages