Photonic Learning in Ultrafast Laser-Induced Complexity

Anthony Nakhoul; Eduardo Brandao; Fayad Ali Banna; Jean-Philippe Colombier; Marc Sebban; R\'emi Emonet

REVIEW 5 major objections 8 minor 54 references

Photonic Learning in Ultrafast Laser-Induced Complexity

T0 review · 5 major / 8 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Repeated ultrafast laser pulses drive a nickel surface to reorganize into nanoscale patterns that absorb more light — a process the paper calls photonic learning, with pulse history stored in the surface shape.

desk verdict A striking experimental survey of laser-induced surface complexity, but the shared uncalibrated SEM-to-height pipeline makes the quantitative learning claim underdetermined. read the letter →

arxiv 2507.08825 v2 pith:RBMULXPE submitted 2025-06-30 physics.optics

Fayad Ali Banna , Eduardo Brandao , Anthony Nakhoul , Rémi Emonet , Marc Sebban , Jean-Philippe Colombier This is my paper · ORCID

classification physics.optics

keywords ultrafastlasersurfacestructuringlaser-inducedself-organizationdissipativestructuresenergyabsorptionLMCcomplexitymutualinformationSwift-Hohenbergequationstructuralmemory

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that a metal surface repeatedly struck by cross-polarized ultrafast laser pulses does not merely roughen or erode: it reorganizes itself to capture more light. Across four irradiation series on nickel, both the absorbed energy $E_{\mathrm{abs}}$ and a measure of structural complexity $C$ rise together through four phases — a quiet response, a sharp learning phase, a plateau of stabilized memory, and a final chaotic destruction — which the authors read as a learning trajectory. The central claim is that the imprints left by light act as structural memory: surface topography encodes pulse history, becomes most sensitive to the exact polarization it was trained on, and settles into configurations finely tuned for energy absorption. The evidence combines numerical solutions of Maxwell's equations on reconstructed surface height maps, a complexity measure derived from a single image, and perturbation tests in which random edits to a pattern reduce absorption while physically consistent edits do not. If the claim holds, laser-written surfaces can be viewed and designed as adaptive optical receptors whose history is stored in their shape.

What carries the argument

The argument is carried by four connected tools. A grayscale-to-height reconstruction converts SEM pixel brightness into a surface height map $u_N(x,y)$, assigning the brightest pixels to peaks (+1) and the darkest to valleys ($-1$). A finite-difference time-domain solution of Maxwell's equations, accelerated by a U-Net surrogate model trained on FDTD outputs, computes the absorbed-energy map $\varepsilon_{\mathrm{abs}}(x,y)$ at six polarization angles $\alpha = k\pi/6$. Taylor-LMC complexity extends the LMC complexity $C = H \times D$ to the sign patterns of the height field and its derivatives up to second order, allowing a single image to stand in for the dynamical complexity of the pattern under an ergodicity assumption. Finally, mutual information between $E_{\mathrm{abs}}$ and $C$ partitions the pulse count axis into four phases, and Swift-Hohenberg patches — solutions of $\partial_t u = r u - (q_0^2+\nabla^2)^2 u + \gamma u^2 - u^3$ — serve as physically plausible edits in the perturbation test that probes how precisely the patterns are tuned.

What would settle it

Measure the true topography of the same nickel surfaces at each pulse count with atomic force microscopy or interferometry and recompute $E_{\mathrm{abs}}$ and the Taylor-LMC complexity from those measured heights; if the four-phase correlation and the polarization anisotropy disappear, the learning signature is an artifact of the grayscale conversion. As a second check, track a single sample region through repeated pulses and test whether complexity and absorption rise on that one evolving spot rather than only across the population of spots.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that self-organized nanostructures formed by repeated ultrafast laser irradiation are a consequence of a learning process: matter gradually adapts to the laser pulses by rearranging itself into the structures that locally maximize absorbed energy. The analysis tracks $E_{\mathrm{abs}}$ and the Taylor-LMC complexity $C$ as functions of the pulse count $N$, and shows they rise together through a learning regime made of a response phase, an iterative learning phase, and a memory stabilization phase, then decouple when prolonged exposure triggers a chaotic destruction phase. Mutual information between $E_{\mathrm{abs}}$ and $C$ marks the phase boundaries. The surfaces also become polarized memories: absorption is systematically highest and lowest at the two experimental polarization angles, and replacing part of a maximal-absorption pattern with arbitrary patches reliably lowers absorption, showing the patterns are functionally precise. The authors assimilate the dynamics to sensitization, a form of non-associative learning, and to plant photomorphogenesis, while stating plainly that materials do not consciously learn.

Load-bearing premise

The whole quantitative result rests on equating the grayscale brightness of SEM images with surface height, with no calibration of that mapping against measured topography, and on reconstructing the pulse-to-pulse history from images taken at different sample spots rather than watching a single region evolve.

Editorial extensions

If this is right

The four-phase trajectory — response, iterative learning, memory stabilization, destruction — should reappear in other laser-patterning series, with the mutual-information curve fixing where each phase begins and ends.
A surface trained by cross-polarized double pulses becomes an anisotropic absorber: the two experimental polarization angles become the directions of maximum and minimum absorption, so the polarization history can be read back from the topography.
Self-organized patterns sit at a functional optimum: random local edits lower absorbed energy, while edits consistent with Swift-Hohenberg dynamics leave absorption nearly unchanged.
Absorption does not simply saturate with pulse count; it feeds a feedback loop — complexity drives absorption and absorption fuels complexity — until the system crosses a threshold into a chaotic regime.
The material response parallels non-associative learning: repeated exposure sensitizes the surface to the stimulus, and hysteresis makes the acquired structure irreversible.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

This suggests a testable design rule the paper does not state: the mutual-information plateau could be used as a training schedule indicator, with pulse sequences stopped near the peak of phase III to imprint a desired absorption profile, and polarization angle acting as the write channel.
The framework predicts a scaling law one could measure: the pulse count at which the learning-to-chaos transition occurs should shift systematically with fluence and delay, since stronger drive should shorten the memory-stabilization plateau; the four series already hint at this ordering.
One could invert the perturbation experiment into a generative tool: search the space of Swift-Hohenberg-reachable patterns for those that maximize absorption at a chosen polarization, converting the learning metaphor into an inverse-design method for laser surface patterning.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague—

The paper is worth your time, but read the quantitative claims with one eye on the metrology. The new results are the four series of nickel surfaces under cross-polarized double pulses, the measured Eabs-complexity correlation across N, and the polarization-dependent absorption bound where the experimental angles α=0 and π/2 bracket the other angles. Those are concrete, reproducible-looking observations. The perturbation experiment—replacing patches with random same-series patches versus Swift-Hohenberg patches—is a clever way to test whether the self-organized patterns are locally optimal for absorption. The authors also disclose, in the text, that pulse-to-pulse evolution is not observed on the same spot; they reconstruct it from different sample locations.

The soft spot is exactly where the stress-test note points. Both Eabs and complexity C are computed from the same synthesized height field uN, obtained by linearly rescaling SEM pixel intensity to height with no calibration. SEM intensity is affected by edge contrast, charging, detector geometry—it is not a height map. If the true height is a monotonic but nonlinear function of grayscale, then the derivative sign patterns feeding Taylor-LMC complexity, and the height profile feeding the Maxwell solver, both shift. The Eabs-C correlation and the phase structure could then be an artifact of shared grayscale statistics. This is not a fatal objection, because the qualitative trend is visible in the raw images and the Maxwell solver is independent in principle, but it is load-bearing for the "learning" conclusion.

Two more secondary issues. The U-Net surrogate is used for thousands of surfaces but no validation accuracy or error bars are reported. And the four phases are defined post hoc from the mutual information of the same two curves they are used to explain; that is circular in the labeling sense, not in the physics sense.

If I were refereeing, I would ask for AFM height maps on a subset of the same locations, a calibration curve for the grayscale-to-height conversion, validation statistics for the surrogate, and a pre-registered criterion for phase boundaries. Those are all doable. The paper is honest about the same-spot limitation and does not oversell the biological analogy—they explicitly say "learn" is used in the broad sense.

Who gets value: the ultrafast-laser-patterning community gets a useful dataset and a design heuristic (stop at the absorption/complexity plateau). Complexity scientists get a nice application of Taylor-LMC.

Bottom line: this deserves a serious referee, and a revised version could be a solid contribution. I would not cite the quantitative learning claim as it stands, but I would cite the experimental series and the polarization bounds.

Recommendation: send to peer review, with the calibration and validation issues as mandatory revision.

Referee Report

5 major / 8 minor

Summary. The paper studies nanoscale patterns generated on a nickel surface by repeated ultrafast laser double-pulse irradiation, using SEM images of surfaces produced with different pulse counts N. The authors convert SEM grayscale to a height map u_N(x,y), compute the absorbed energy E_abs with an FDTD Maxwell solver approximated by a U-Net surrogate, and quantify morphological complexity with a Taylor-LMC measure. They report that E_abs and complexity rise together through four phases while mutual information between them peaks and then falls, that the experimental polarization angles become the upper and lower bounds of polarization-dependent absorption, and that randomly perturbing the most absorbing patterns decreases absorption while Swift-Hohenberg-style perturbations have a smaller effect. These observations are interpreted as evidence that the surface 'learns' to maximize absorbed energy through a feedback-driven adaptation process analogous to biological sensitization.

Significance. The paper is ambitious and addresses a timely topic: whether dissipative structure formation under repeated laser irradiation can be framed as a learning process. If the quantitative pipeline were validated, the reported coupling between morphological complexity and optical absorption would be a useful design principle for laser surface structuring. The manuscript has notable strengths: the absorption calculation is grounded in an independent Maxwell solver, the same-spot limitation is explicitly acknowledged, and the perturbation study uses a large number of surfaces. However, the current evidence does not yet establish the central claim because key calibrations and validations are missing.

major comments (5)

[Results, 'Experimental self-organization' and Methods, 'Maxwell equations surrogate model'] The uncalibrated SEM grayscale-to-height conversion is load-bearing. The text assigns brightest pixels to +1 and darkest to 0, and this same height map u_N(x,y) is used both for the FDTD/U-Net computation of E_abs and for the Taylor-LMC complexity C (Eq. 7). SEM intensity is not a linear proxy for height; it is affected by edge effects, charging, and detector geometry, and no calibration to true topography is provided (AFM is mentioned only for one nanopeak). A monotonic nonlinear transform of the grayscale values can alter sign patterns of the derivatives and hence C, and it changes the surface profile in the FDTD simulation and hence E_abs. The correlation in Fig. 2 and the polarization trends in Fig. 3 could therefore be artifacts of the shared grayscale statistics rather than a physical absorption-complexity feedback. Provide an AFM or profilometry calibration of the intensity-to-height transfer function and a sensitivity analysis under monotonic nonlinear rescalings.
[Methods, 'Ultrafast laser setup'] The memory claim is based on different sample locations. The paper explicitly states that coordinates of u_N and u_{N+1} do not coincide and that real-time local pulse-to-pulse evolution is absent, yet the abstract and conclusions claim that 'memory of previous irradiations is encoded as surface topography evolves' and that matter 'gradually adapts to laser pulses by rearranging itself.' The reconstruction of a learning trajectory from different spots requires the assumption that all spots follow the same dynamical path; this assumption is not tested. Show that within-N variability across the several spots is small compared with the between-N trends in Fig. 2, or restrict the claims to global organizational statistics rather than a location-wise history.
[Methods, 'Maxwell's equations surrogate model'] No validation of the U-Net surrogate is reported. E_abs curves, the mutual information in Fig. 2b, and the perturbation experiments in Fig. 4 all rely on surrogate predictions, yet the reader has no information about the surrogate's error against direct FDTD solutions. Report test-set accuracy (e.g., mean absolute error and R^2 on a held-out set of surfaces and polarization angles), and provide error bars on E_abs propagated from surrogate uncertainty.
[Results, 'Effect of modifying the structure of a pattern P that maximizes energy absorption' and Fig. 4] The perturbation study is biased by the selection of P as the pattern that maximizes E_abs in each series. For random patch replacement, the observation that almost all perturbed patterns have lower E_abs is an expected property of a maximum, not independent evidence that the self-organized pattern is finely tuned. A null model using typical, non-maximal patterns from the same series is needed to determine whether the drop is larger than expected for any pattern with the same morphology statistics. Without such a comparison, the claim that the surfaces are 'resilient to perturbations' and 'finely tuned' is not supported.
[Methods, 'Taylor-LMC complexity' and Fig. 2] The four-phase structure is read off the same E_abs and C curves that it is used to explain. The mutual information MI in Fig. 2b is computed from E_abs and C and then used to split the curves into phases I-IV, and the phases are subsequently interpreted as a learning trajectory. This is a descriptive classification, not an independent test. The paper should state this explicitly and test robustness of the phase boundaries to alternative complexity measures or MI estimation parameters.

minor comments (8)

[Methods, 'Solving Maxwell's equations in inhomogeneous surface'] The Maxwell equation ∇×H = ∂D/∂t omits the conduction current; for a metal like nickel this may be absorbed in the complex permittivity, but the notation should be clarified.
[Methods, 'Solving Maxwell's equations in inhomogeneous surface'] The laser wavelength used in the FDTD simulations is not specified; the nickel refractive index n~=2.15+4.3i depends on wavelength.
[Methods, 'Creating artificial surfaces'] The Swift-Hohenberg parameters r, γ, q0 used to generate the artificial surfaces PSH are not given in the text, so the claim that they are physically plausible cannot be assessed.
[Methods, 'Taylor-LMC complexity'] In Eq. (7), the notation T nC(P u) is confusing: the superscript n appears as a subscript-like 'T nC', and the displayed expansion for T^2C uses terms C(P u), C(P ux), etc., without explicit factorial denominators. Define the notation clearly and consistently.
[Methods, 'Mutual information'] The mutual information estimator in Eq. (4) is written as a sum over e,c, implying discrete histograms, but no binning strategy is described; specify the histogram parameters or the density estimation method.
[Results, 'Effect of laser polarization angle α on absorbed energy'] In Fig. 3, the shaded region for α=0 and π/2 is defined as standard deviation across different locations; state the number of locations and whether the same locations are used for each N.
[Global] There are several typographical issues, including the heading 'RESUL TS' and inconsistent spacing in 'M I'.
[Abstract] The abstract's phrase 'unravel the nature of learning schemes' is stronger than what the paper delivers; consider softening to 'investigate'.

Circularity Check

2 steps flagged · score 4.0 of 10

Learning-regime correlation is defined by the MI criterion, and the 'optimization' perturbation result is selected from the Eabs maximum; the Maxwell-solver-based core remains independent.

self definitional [Results, 'Complexity drives absorption and absorption fuels complexity' (Fig. 2 and Mutual Information paragraph)]
"MI serves as the criterion to frame Eabs and C variation into the four previously mentioned phases. ... In the learning regime, absorbed energy correlates strongly with pattern complexity under fixed laser conditions."

The four phases, including the 'learning regime', are defined by thresholding the mutual information MI(Eabs; C) computed from the same Eabs and C time series. The claim that Eabs and C are strongly correlated 'in the learning regime' is therefore not an independent empirical finding: the learning regime is selected as the N-window in which MI is high. That correlation is true by construction for that window. The subsequent inference that 'complexity drives absorption and absorption fuels complexity' rests on this self-selected correlation rather than on an independent test.
fitted input called prediction [Results, Fig. 4 and 'To evaluate the effect of perturbing an experimental pattern P...']
"To evaluate the effect of perturbing an experimental pattern P, that maximizes the absorbed energy Eabs for a given series i, we propose two distinct methods. ... This region consistently falls below the reference line, regardless of the similarity between the experimental and modified surfaces. ... demonstrating that they are finely tuned to optimize performance."

P is explicitly selected as the pattern maximizing Eabs among the available N (e.g., P1 at N=31, P2 at N=20). The reference line in Fig. 4 is Eabs(P). Showing that random perturbations of P give Eabs(P') < Eabs(P) is statistically forced by the selection rule: a maximum is expected to sit above nearby points. The conclusion that the experimental structures are 'finely tuned to optimize performance' is therefore not a first-principles prediction; it is a restatement of the maximization used to define P.

full rationale

The paper's quantitative core is largely self-contained: Eabs comes from an independent FDTD Maxwell solver (with a U-Net surrogate), C is defined via the Taylor-LMC complexity measure, and the polarization-angle ordering is an empirical observation computed from those Eabs maps. However, two supporting arguments are partially circular. First, the 'learning regime' is defined as the N-window where MI(Eabs; C) is high, so the observation that Eabs and C are correlated in that regime is true by definition. Second, the perturbation experiment in Fig. 4 selects P as the Eabs-maximizing pattern, so demonstrating that perturbing P reduces Eabs is a consequence of the selection rather than a physical prediction. These issues weaken the 'learning' interpretation and the 'finely tuned' claim, but they do not eliminate the independent Maxwell-solver results. The uncalibrated SEM-grayscale-to-height mapping is a significant correctness risk (shared input to both Eabs and C), but it is not circularity because Eabs and C are independently computed functions of that input. Overall, the central claim retains independent content, but the score reflects the partial definitional character of two key supporting steps.

Assumptions & free parameters 4 free parameters · 7 assumptions · 0 invented entities

The central claim rests on several choices the reader does not independently verify: a grayscale-to-height conversion, the authors' own complexity metric, a neural surrogate, Swift-Hohenberg patch generation, and post hoc phase boundaries. The Maxwell solver provides external physics for absorption, which keeps the analysis from being purely definitional.

free parameters (4)

SEM grayscale-to-height mapping = linear scaling of 8-bit intensity to [-1,1]
Chosen without calibration; all Maxwell and complexity calculations use this synthesized height field uN(x,y).
U-Net surrogate weights = not disclosed
Trained on FDTD outputs and used for the 8000 artificial surfaces; training set, hyperparameters and validation error are not reported.
Swift-Hohenberg parameters (r, gamma, q0) = not reported
Used to generate physically plausible patches PSH; without values the perturbation ensemble is not reproducible.
Phase boundaries for regimes I-IV = per-series N values inferred from mutual information curve
The learning/chaotic segmentation is read off the same Eabs and C curves it is used to explain.

assumptions (7)

domain assumption SEM pixel intensity is proportional to surface height via a linear grayscale conversion
Underpins uN(x,y) used in FDTD and complexity; no calibration against AFM topography is provided.
domain assumption Images at different sample positions for different N represent the same local pulse-to-pulse trajectory
The paper states that (x,y) coordinates of uN and uN+1 do not coincide, so the learning trajectory is inferred from different impacts.
domain assumption Each SEM image can be treated as a long-term evolution of slightly perturbed initial conditions (ergodicity)
Methods section: 'we assume ergodicity and regard each image as the long-term evolution of a collection of slightly perturbed initial conditions.'
ad hoc to paper Taylor-LMC-n complexity from sign patterns of u and derivatives up to second order is a valid proxy for dynamical complexity
Introduced in co-author's thesis and prior PRL [35,37]; no independent validation that this scalar tracks actual dynamical complexity.
domain assumption Self-organized laser patterns can be modeled by the Swift-Hohenberg equation
Used to create artificial surfaces PSH; relies on cited prior work [37] rather than derivation in this paper.
domain assumption The U-Net surrogate accurately reproduces FDTD absorption for unseen surfaces
Surrogate predictions drive the 8000-surface perturbation study; no accuracy metrics are reported.
domain assumption Room-temperature nickel optical constants remain valid in the absorption calculation
FDTD uses fixed n = 2.15 + 4.3i without modeling temperature or phase changes in permittivity.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Photonic Learning in Ultrafast Laser-Induced Complexity." pith.science (2026). https://pith.science/paper/RBMULXPE

@misc{pith2026250708825,
  author       = {Pith},
  title        = {Pith review of: Photonic Learning in Ultrafast Laser-Induced Complexity},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RBMULXPE}},
  note         = {Machine review of arXiv:2507.08825}
}

read the original abstract

How can one design complex systems capable of learning for a given functionality? In the context of ultrafast laser-surface interaction, we unravel the nature of learning schemes tied to the emergence of complexity in dissipative structures. The progressive development of learning mechanisms, from direct information storage to the development of smart surfaces, originates from the network of curvatures formed in the unstable fluid under thermoconvective instability, which is subsequently quenched and resolidified. Under pulsed laser irradiation, non-equilibrium dynamics generate intricate nanoscale patterns, unveiling adaptive process mechanisms. We demonstrate that the imprints left by light act as a form of structural memory, encoding not only local effects directed by laser field polarization but also a cooperative strategy of reliefs that dynamically adjust surface morphology to optimize light capture. By investigating how apparent complexity and optical response are intricately intertwined, shaping one another, we establish a framework that draws parallels between material adaptation and learning dynamics observed in biological systems.

Figures

Figures reproduced from arXiv: 2507.08825 by the authors.

**Figure 2.** b illustrates the progression of mutual information MI between the absorbed energy Eabs and the complexity C with the number of double pulses N. MI quantifies the dependency between two variables, capturing both linear and non-linear relationships. A high MI indicates strong dependence between Eabs and C, while a low value suggests weak or no relationship. MI serves as the criterion to frame Eabs and C variation… view at source ↗

**Figure 3.** FIG. 3 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: FIG. 4 [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 52 canonical work pages

[1]

Wiener, Cybernetics or Control and Communication in the Animal and the Machine (MIT press, 2019)

N. Wiener, Cybernetics or Control and Communication in the Animal and the Machine (MIT press, 2019)

work page 2019
[2]

Bonse, S

J. Bonse, S. V. Kirner, and J. Kr¨ uger, Laser-induced pe- riodic surface structures (lipss), Handbook of laser micro- and nano-engineering , 1 (2020)

work page 2020
[3]

Zhang, J.-P

H. Zhang, J.-P. Colombier, C. Li, N. Faure, G. Cheng, and R. Stoian, Coherence in ultrafast laser-induced pe- riodic surface structures, Physical Review B 92, 174109 (2015)

work page 2015
[4]

J. S. Hansen, J. C. Dyre, P. Daivis, B. D. Todd, and H. Bruus, Continuum nanofluidics, Langmuir 31, 13275 (2015)

work page 2015
[5]

For a given series, after N double pulses, these maps show variations based on the alignment of the structures with the electric field ⃗E

Experimentally, self-organized structures are obtained using cross-polarized double pulses oriented at α = 0 and α = π 2 , which we refer to as the experimental polarization angles, making them particularly significant. For a given series, after N double pulses, these maps show variations based on the alignment of the structures with the electric field ⃗E...

work page
[6]

Rudenko and J.-P

A. Rudenko and J.-P. Colombier, How light drives mate- rial periodic patterns down to the nanoscale, in Ultrafast Laser Nanostructuring: The Pursuit of Extreme Scales (Springer, 2023) pp. 209–255

work page 2023
[7]

Rudenko, C

A. Rudenko, C. Mauclair, F. Garrelie, R. Stoian, and J.-P. Colombier, Self-organization of surfaces on the nanoscale by topography-mediated selection of quasi- cylindrical and plasmonic waves, Nanophotonics 8, 459 (2019)

work page 2019
[8]

Trice, C

J. Trice, C. Favazza, D. Thomas, H. Garcia, . f. R. Kalya- naraman, and R. Sureshkumar, Novel self-organization mechanism in ultrathin liquid films: theory and experi- ment, Physical review letters 101, 017802 (2008)

work page 2008

Show all 54 references

[9]

T.-H. Her, R. J. Finlay, C. Wu, S. Deliwala, and E. Mazur, Microstructuring of silicon with femtosecond laser pulses, Applied Physics Letters 73, 1673 (1998)

1998
[10]

R. R. Gattass and E. Mazur, Femtosecond laser micro- machining in transparent materials, Nature photonics 2, 219 (2008)

2008
[11]

Sugioka and Y

K. Sugioka and Y. Cheng, Ultrafast laser processing: from micro-to nanoscale (CRC Press, 2013)

2013
[12]

Ilday, G

S. Ilday, G. Makey, G. B. Akguc, ¨O. Yavuz, O. Tokel, I. Pavlov, O. G¨ ulseren, and F. ¨O. Ilday, Rich complex behaviour of self-assembled nanoparticles far from equi- librium, Nature communications 8, 14942 (2017)

2017
[13]

Makey, S

G. Makey, S. Galioglu, R. Ghaffari, E. D. Engin, G. Yıldırım, ¨O. Yavuz, O. Bekta¸ s,¨U. S. Nizam, ¨O. Ak- bulut, ¨O. S ¸ahin,et al. , Universality of dissipative self- assembly from quantum dots to human cells, Nature Physics 16, 795 (2020)

2020
[14]

Zorba, E

V. Zorba, E. Stratakis, M. Barberoglou, E. Spanakis, P. Tzanetakis, S. H. Anastasiadis, and C. Fotakis, Biomimetic artificial surfaces quantitatively reproduce the water repellency of a lotus leaf, Advanced materials 20, 4049 (2008). 10

2008
[15]

Stratakis, J

E. Stratakis, J. Bonse, J. Heitz, J. Siegel, G. D. Tsi- bidis, E. Skoulas, A. Papadopoulos, A. Mimidis, A.-C. Joel, P. Comanns, J. Kr¨ uger, C. Florian, Y. Fuentes- Edfuf, J. Solis, and W. Baumgartner, Laser engineering of biomimetic surfaces, Materials Science and Engineer- i...

2020
[16]

A. Baev, P. N. Prasad, H. ˚Agren, M. Samo´ c, and M. We- gener, Metaphotonics: An emerging field with opportu- nities and challenges, Physics Reports 594, 1 (2015)

2015
[17]

Shimotsuma, P

Y. Shimotsuma, P. G. Kazansky, J. Qiu, and K. Hi- rao, Self-organized nanogratings in glass irradiated by ultrashort light pulses, Physical review letters 91, 247405 (2003)

2003
[18]

H. Yao, Q. Xie, M. Cavillon, Y. Dai, and M. Lancry, Materials roadmap for inscription of nanogratings inside transparent dielectrics using ultrafast lasers, Progress in Materials Science , 101226 (2023)

2023
[19]

Stoian and J.-P

R. Stoian and J.-P. Colombier, Advances in ultrafast laser structuring of materials at the nanoscale, Nanophotonics 9, 4665 (2020)

2020
[20]

Prigogine and P

I. Prigogine and P. Van Rysselberghe, Introduction to thermodynamics of irreversible processes, Journal of The Electrochemical Society 110, 97C (1963)

1963
[21]

Rudenko, A

A. Rudenko, A. Abou-Saleh, F. Pigeon, C. Mauclair, F. Garrelie, R. Stoian, and J.-P. Colombier, High- frequency periodic patterns driven by non-radiative fields coupled with Marangoni convection instabilities on laser- excited metal surfaces, Acta Materialia 194, 93 (2020)

2020
[22]

G. D. Tsibidis, C. Fotakis, and E. Stratakis, From rip- ples to spikes: A hydrodynamical mechanism to inter- pret femtosecond laser-induced self-assembled structures, Physical Review B 92, 041405 (2015)

2015
[23]

Perrakis, O

G. Perrakis, O. Tsilipakos, G. D. Tsibidis, and E. Stratakis, Impact of hybrid electromagnetic surface modes on the formation of low spatial frequency lipss: A universal approach, Laser & Photonics Reviews , 2301090 (2024)

2024
[24]

Lu, L.-Y

Y.-W. Lu, L.-Y. Li, and J.-F. Liu, Influence of surface roughness on strong light-matter interaction of a quan- tum emitter-metallic nanoparticle system, Scientific Re- ports 8, 7115 (2018)

2018
[25]

M. C. D ´ ıaz-Barradas, M. Zunzunegui, L. Alvarez- Cansino, M. P. Esquivias, J. Valera, and H. Rodr ´ ıguez, How do mediterranean shrub species cope with shade? ecophysiological response to different light intensities, Plant Biology 20, 296 (2018)

2018
[26]

H. Wang, R. Wang, S. P. Harrison, and I. C. Prentice, Leaf morphological traits as adaptations to multiple cli- mate gradients, Journal of Ecology 110, 1344 (2022)

2022
[27]

Pe˜ nuelas and J

J. Pe˜ nuelas and J. Llusi` a, Bvocs: plant defense against climate warming?, Trends in plant science 8, 105 (2003)

2003
[28]

A. H. Teramura and J. H. Sullivan, Effects of uv-b radi- ation on photosynthesis and growth of terrestrial plants, Photosynthesis research 39, 463 (1994)

1994
[29]

E. M. Middleton and A. H. Teramura, Understanding photosynthesis, pigment and growth responses induced by uv-b and uv-a irradiances, Photochemistry and pho- tobiology 60, 38 (1994)

1994
[30]

Lambers, F

H. Lambers, F. S. Chapin III, and T. L. Pons, Plant physiological ecology(Springer Science & Business Media, 2008)

2008
[31]

Chaves, J

M. Chaves, J. Flexas, and C. Pinheiro, Photosynthesis under drought and salt stress: regulation mechanisms from whole plant to cell, Annals of botany 103, 551 (2009)

2009
[32]

Maier, Plasmonics: Fundamentals and applications (2007)

S. Maier, Plasmonics: Fundamentals and applications (2007)

2007
[33]

V. E. Ferry, A. Polman, and H. A. Atwater, Modeling light trapping in nanostructured solar cells, ACS nano 5, 10055 (2011)

2011
[34]

Zhang, J.-P

H. Zhang, J.-P. Colombier, and S. Witte, Laser-induced periodic surface structures: Arbitrary angles of incidence and polarization states, Physical Review B 101, 245430 (2020)

2020
[35]

Novotny and B

L. Novotny and B. Hecht, Principles of nano-optics (Cambridge university press, 2012)

2012
[36]

Brandao, Complexity Methods in Physics-Guided Ma- chine Learning , Ph.D

E. Brandao, Complexity Methods in Physics-Guided Ma- chine Learning , Ph.D. thesis, Jean Monnet University, France (2024)

2024
[37]

Lopez-Ruiz, H

R. Lopez-Ruiz, H. L. Mancini, and X. Calbet, A statis- tical measure of complexity, Physics letters A 209, 321 (1995)

1995
[38]

Brandao, A

E. Brandao, A. Nakhoul, S. Duffner, R. Emonet, F. Gar- relie, A. Habrard, F. Jacquenet, F. Pigeon, M. Sebban, and J.-P. Colombier, Learning complexity to guide light- induced self-organized nanopatterns, Physical Review Letters 130, 10.1103/physrevlett.130.226201 (2023)

2023 doi
[39]

Bonse and S

J. Bonse and S. Gr¨ af, Maxwell meets marangoni—a re- view of theories on laser-induced periodic surface struc- tures, Laser & Photonics Reviews 14, 2000215 (2020)

2020
[40]

E. R. Kandel, J. H. Schwartz, T. M. Jessell, S. Siegel- baum, A. J. Hudspeth, S. Mack, et al. , Principles of neural science, Vol. 4 (McGraw-hill New York, 2000)

2000
[41]

Poon and D

C.-S. Poon and D. L. Young, Nonassociative learning as gated neural integrator and differentiator in stimulus- response pathways, Behavioral and Brain Functions 2, 1 (2006)

2006
[42]

Grafman, Conceptualizing functional neuroplasticity, Journal of communication disorders 33, 345 (2000)

J. Grafman, Conceptualizing functional neuroplasticity, Journal of communication disorders 33, 345 (2000)

2000
[43]

Nemhauser and J

J. Nemhauser and J. Chory, Photomorphogenesis, The Arabidopsis Book/American Society of Plant Biologists 1 (2002)

2002
[44]

Schmitt, Is photomorphogenic shade avoidance adap- tive? perspectives from population biology, Plant, Cell & Environment 20, 826 (1997)

J. Schmitt, Is photomorphogenic shade avoidance adap- tive? perspectives from population biology, Plant, Cell & Environment 20, 826 (1997)

1997
[45]

Bonse and K

J. Bonse and K. Sokolowski-Tinten, Probing laser-driven structure formation at extreme scales in space and time, Laser & Photonics Reviews 18, 2300912 (2024)

2024
[46]

Aguilar, C

A. Aguilar, C. Mauclair, N. Faure, J.-P. Colombier, and R. Stoian, In-situ high-resolution visualization of laser- induced periodic nanostructures driven by optical feed- back, Scientific Reports 7, 16509 (2017)

2017
[47]

Nakhoul, C

A. Nakhoul, C. Maurice, M. Agoyan, A. Rudenko, F. Garrelie, F. Pigeon, and J.-P. Colombier, Self- Organization Regimes Induced by Ultrafast Laser on Sur- faces in the Tens of Nanometer Scales, Nanomaterials11, 1020 (2021)

2021
[48]

Torrance and J

T. Torrance and J. C. Maxwell, A dynamical theory of the electromagnetic field (1982)

1982
[49]

V. Y. Fedorov and J.-P. Colombier, Light-matter inter- action at rough surfaces: A morphological perspective on laser-induced periodic surface structures, Physical Re- view B 110, 115438 (2024)

2024
[50]

Ronneberger, P

O. Ronneberger, P. Fischer, and T. Brox, U-net: Con- volutional networks for biomedical image segmentation (2015), arXiv:1505.04597 [cs.CV]

2015 arXiv
[51]

Kraskov, H

A. Kraskov, H. St¨ ogbauer, and P. Grassberger, Estimat- ing mutual information, Physical Review E-Statistical, 11 Nonlinear, and Soft Matter Physics 69, 066138 (2004)

2004
[52]

T. M. Cover, J. A. Thomas, et al. , Entropy, relative en- tropy and mutual information, Elements of information theory 2, 12 (1991)

1991
[53]

Bandt and B

C. Bandt and B. Pompe, Permutation entropy: a natu- ral complexity measure for time series, Physical review letters 88, 174102 (2002)

2002
[54]

Swift and P

J. Swift and P. C. Hohenberg, Hydrodynamic fluctua- tions at the convective instability, Physical Review A15, 319 (1977)

1977

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Wiener, Cybernetics or Control and Communication in the Animal and the Machine (MIT press, 2019)

N. Wiener, Cybernetics or Control and Communication in the Animal and the Machine (MIT press, 2019)

work page 2019

[2] [2]

Bonse, S

J. Bonse, S. V. Kirner, and J. Kr¨ uger, Laser-induced pe- riodic surface structures (lipss), Handbook of laser micro- and nano-engineering , 1 (2020)

work page 2020

[3] [3]

Zhang, J.-P

H. Zhang, J.-P. Colombier, C. Li, N. Faure, G. Cheng, and R. Stoian, Coherence in ultrafast laser-induced pe- riodic surface structures, Physical Review B 92, 174109 (2015)

work page 2015

[4] [4]

J. S. Hansen, J. C. Dyre, P. Daivis, B. D. Todd, and H. Bruus, Continuum nanofluidics, Langmuir 31, 13275 (2015)

work page 2015

[5] [5]

For a given series, after N double pulses, these maps show variations based on the alignment of the structures with the electric field ⃗E

Experimentally, self-organized structures are obtained using cross-polarized double pulses oriented at α = 0 and α = π 2 , which we refer to as the experimental polarization angles, making them particularly significant. For a given series, after N double pulses, these maps show variations based on the alignment of the structures with the electric field ⃗E...

work page

[6] [6]

Rudenko and J.-P

A. Rudenko and J.-P. Colombier, How light drives mate- rial periodic patterns down to the nanoscale, in Ultrafast Laser Nanostructuring: The Pursuit of Extreme Scales (Springer, 2023) pp. 209–255

work page 2023

[7] [7]

Rudenko, C

A. Rudenko, C. Mauclair, F. Garrelie, R. Stoian, and J.-P. Colombier, Self-organization of surfaces on the nanoscale by topography-mediated selection of quasi- cylindrical and plasmonic waves, Nanophotonics 8, 459 (2019)

work page 2019

[8] [8]

Trice, C

J. Trice, C. Favazza, D. Thomas, H. Garcia, . f. R. Kalya- naraman, and R. Sureshkumar, Novel self-organization mechanism in ultrathin liquid films: theory and experi- ment, Physical review letters 101, 017802 (2008)

work page 2008