Skip to content

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
Before references / appendix: ≈ 17.15%References / intervening material: ≈ 13.03%Attached appendix: ≈ 69.82%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.
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 headingStarts on PDF pageEstimated spanShare of entire PDF
1 INTRODUCTION1≈ 1.67 pp.2.78%
2 SOLVING TIME-DEPENDENT PDES USING FROZEN-PINNS3≈ 2.33 pp.3.88%
3 EMPIRICAL RESULTS5≈ 3.23 pp.5.38%
4 CONCLUSION8≈ 2.31 pp.3.85%
ACKNOWLEDGMENTS11≈ 0.18 pp.0.3%
REFERENCES11≈ 7.81 pp.13.02%
APPENDIX19≈ 2 pp.3.33%
A EXTENDED REVIEW OF RELATED WORK21≈ 1.51 pp.2.52%
B SUPPLEMENTARY METHODOLOGICAL DETAILS ON PDE SOLVERS22≈ 10.26 pp.17.1%
C SUPPLEMENTARY DETAILS ON NUMERICAL EXPERIMENTS32≈ 28.13 pp.46.88%
All detected figure and table locations

Argument and organization

  1. Two bottlenecks and space–time separation

    Motivates fixed spatial features with evolving output coefficients.

  2. Constructing and solving the reduced system

    Adds sampling, boundary handling, SVD compression and an explicit training algorithm.

  3. 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