Nonlinear Interaction Research Articles

Data-driven risk analysis of nonlinear factor interactions in road safety using Bayesian networks

This paper aims to demonstrate nonlinear risk factor interactions based on a data-driven approach using a Bayesian network model, providing a road safety use case. Road safety is a critical issue worldwide, with approximately 1.3 million road traffic deaths each year (WHO). Traditional road safety risk assessment methods often analyze individual factors separately; however, these assessments fail to capture the complex dynamics of real-world analysis, in which multiple factors interact through nonlinear relationships. In this study, a novel road safety risk assessment approach that uses a Bayesian network model to explore the nonlinear relationships among road safety risk factors is developed. Through the analysis of extensive crash reports from the state of Maryland, the complex interdependencies among various risk factors and their cumulative impact on road safety are investigated. Our findings show that two combined risk factors have different effects on risk level when considered individually. Two case studies related to human state risk factors and environmental risk factors, such as driving under the influence and snowy roads, as well as fatigue and snowy roads, have an amplified effect on the risk level. The findings highlight the importance of considering nonlinear interactions among risk factors when developing effective and targeted strategies for accident prevention and road safety improvement. This research contributes to the field of road safety by presenting a new methodology for understanding and mitigating road safety hazards.

A multi-peak performance landscape for scale biting in an adaptive radiation of pupfishes.

The physical interactions between organisms and their environment ultimately shape diversification rates, but the contributions of biomechanics to evolutionary divergence are frequently overlooked. Here, we estimated a performance landscape for biting in an adaptive radiation of Cyprinodon pupfishes, including scale-biting and molluscivore specialists, and compared performance peaks with previous estimates of the fitness landscape in this system. We used high-speed video to film feeding strikes on gelatin cubes by scale eater, molluscivore, generalist and hybrid pupfishes and measured bite dimensions. We then measured five kinematic variables from 227 strikes using the SLEAP machine-learning model. We found a complex performance landscape with two distinct peaks best predicted gel-biting performance, corresponding to a significant non-linear interaction between peak gape and peak jaw protrusion. Only scale eaters and their hybrids were able to perform strikes within the highest performance peak, characterized by larger peak gapes and greater jaw protrusion. A performance valley separated this peak from a lower performance peak accessible to all species, characterized by smaller peak gapes and less jaw protrusion. However, most individuals exhibited substantial variation in strike kinematics and species could not be reliably distinguished by their strikes, indicating many-to-many mapping of morphology to performance. The two performance peaks observed in the lab were partially consistent with estimates of a two-peak fitness landscape measured in the wild, with the exception of the new performance peak for scale eaters. We thus reveal a new bimodal non-linear biomechanical model that connects morphology to performance to fitness in a sympatric radiation of trophic niche specialists.

Spontaneous emergence of temporal structures in a continuously driven many-body system

Abstract The spontaneous emergence of temporal structures challenges the conventional understanding that systems governed by time-invariant laws remain unchanged over time. Recent experiments have observed this time translation symmetry breaking in quantum atomic systems that either exhibit strong atom-atom interactions or have low dissipation rates. While current theoretical frameworks reveal the importance of strong atom-atom interactions, they fall short in explaining this phenomenon observed in low-dissipation atomic systems. Here, we present a theoretical study on the spontaneous breaking of time translation symmetry in materials with low
dissipation rates. By constructing phase diagrams for a system of four-level atoms driven by a continuous-wave optical field, we identify the essential requirements for self-sustained temporal motions. These include a driven open system, nonlinear interactions, and sufficient degrees of freedom that facilitate competing processes. Our findings contribute to a better understanding of the emergence of spontaneous time translation symmetry breaking in these materials.

The Application of an Intermediate Complexity Atmospheric Research Model in the Forecasting of the Henan 21.7 Rainstorm

To improve the forecast accuracy of heavy precipitation, re-forecasts are conducted for the Henan 21.7 rainstorm. The Intermediate Complexity Atmospheric Research Model (ICAR) and the Weather Research and Forecasting Model (WRF) with a 1 km horizontal grid spacing are used for the re-forecasts. The results indicate that heavy precipitation forecasted by ICAR primarily accumulates on the windward slopes of the mountains. In contrast, some severe precipitation forecasted by WRF is beyond the mountains. The main difference between ICAR and WRF is that ICAR excludes the “impacts of physical processes on winds and the nonlinear interactions between the small resolvable-scale disturbances” (briefed as the “physical–dynamical interactions”). Thus, heavy precipitation beyond the mountains is attributed to the “physical–dynamical interactions”. Furthermore, severe precipitation on the windward slopes of the mountains typically aligns with the observations, whereas heavy rainfall beyond the mountains seldom matches the observations. Therefore, severe precipitation on the windward slopes of (beyond) the mountains is more (less) predictable. Based on these findings and theoretical thinking about the predictability of severe precipitation, a scheme of using the ICAR’s prediction to adjust the WRF’s prediction is proposed, thereby improving the forecast accuracy of heavy rainfall.

Unveiling Climate–Land Use and Land Cover Interactions on the Kerch Peninsula Using Structural Equation Modeling

This paper examines the effects of climatic factors, specifically temperature and precipitation, on land use and land cover (LULC) on the Kerch Peninsula using structural equation modeling (SEM). The Normalized Difference Vegetation Index (NDVI) was used as a mediator in the model to accurately assess the impact of climate change on vegetation and subsequent LULC dynamics. The results indicate that temperature exerts a significant negative influence on LULC in the early periods, inducing stress on vegetation and leading to land degradation. However, this influence diminishes over time, possibly due to ecosystem adaptation and the implementation of resilient land management practices. In contrast, the impact of precipitation on LULC, which is initially minimal, increases significantly, highlighting the need for improved water resource management and adaptation measures to mitigate the negative effects of excessive moisture. The NDVI plays a crucial mediating role, reflecting the health and density of vegetation in response to climatic variables. An analysis of lagged effects shows that both precipitation and temperature exert delayed effects on LULC, underscoring the complexity of water dynamics and ecosystem responses to climatic conditions. These results have important practical implications for land resource management and climate adaptation strategies. Understanding the nuanced interactions between climatic factors and LULC can inform the development of resilient agricultural systems, optimized water management practices, and effective land use planning. Future research should focus on refining models to incorporate nonlinear interactions, improving data accuracy, and expanding the geographic scope to generalize findings. This study highlights the importance of continuous monitoring and adaptive management to develop sustainable land management practices that can withstand the challenges of climate change.

Role of charge injection/de-trapping in imprint behavior of ferroelectric Hf0.5Zr0.5O2 thin film

Ferroelectric HfO2-based thin films have attracted increasing interest due to their potential applications in nonvolatile memory areas. However, their unique properties, such as imprint, lead to serious reliability issues. In this study, the origin of imprint behavior is investigated by using TiN/Hf0.5Zr0.5O2 (10 nm)/TiN MFM capacitors. By modulating the internal electric field during imprint, we discover that charge injection/de-trapping in metal–ferroelectric interfaces plays an essential role in imprint behavior rather than charge movement inside the FE layer. The asymmetric shift of the coercive field is also discussed, which is attributed to nonlinear interaction between polarization's bound charges and the electrode's screening charges. This suggests that controlling interface quality should effectively suppress imprint in HfO2-based thin films.

Role of inertial nonlinearity and coupling stiffness on a series of coupled harvesters

This study investigates the effect of coupling configuration on a state-of-the-art harvesting system consisting of a series of spring-coupled piezoelectric nonlinear monostable tip-loaded cantilever-based vibration energy harvesters (VEHs). The investigation finds favorable coupling configurations that exploit complex nonlinear interaction for improving system performance. The system model incorporates the beam's geometric nonlinearities in the governing equation using inertial and stiffness nonlinear terms. The study first analytically investigates the effect of grounding stiffness on a single harvester's performance. Then, it numerically investigates the effect of increasing array size and changing stiffness distribution. The study discovers a novel hardening/softening transition phenomenon at a critical grounding stiffness value induced by the inertial nonlinear effect. This critical event also maximizes peak output power near the largest eigenfrequency at an optimum grounding stiffness value. At larger arrays, the peak power shows a complex bifurcation behavior, hinting at the presence of multiple attractors. The numerical study of different stiffness configurations and linearized eigenanalysis reveals the necessity of a multilevel optimization strategy focused on both the stiffness ratio between the coupling springs' and their overall stiffness level. This study bridges the gap between previous studies on spring-coupled linear and bistable arrays, offering new insights into coupled nonlinear interactions and proposing novel optimization strategies.

[formula omitted]-symmetry induced bi-stability in non-Hermitian cavity magnomechanics

We study the steady-state non-Hermitian magnomechanical system driven by a transverse magnetic field directly interacting with YIG sphere yielding in magnons excitation, which later interacts with cavity microwave photons. To make the system non-Hermitian, we use a traveling field directly interacting with magnons generating gain to the system. We start by illustrating PT-configuration of the system containing two PT broken regions around exceptional point and PT protected region along the axis of exceptional point. We discover that the steady-state population of cavity photons and magnons show bistable behavior depending upon the PT configuration, which becomes more significant as the values of the magnon–photon coupling and traveling field strength increases. We illustrate that steady-state photon only shows bistable behavior when the system in lossy PT broken configuration, means strength of traveling field is less than the magnon–photon coupling. Otherwise, it will just contain a single stable state because of bistability suppression with gain in the system, which is unlike with any other investigation in this direction. Further, a larger magnon–photon coupling increases photon intensity and decreases magnon intensity, because of photon and magnon energy exchange, leading to enhanced photon bistablity and decreased magnon bistability. However, in case of increasing strength of traveling field, both photon as well as magnon bistability is appeared to be decreasing. We also study the steady-state effective potential of the system and illustrate the occurrence of bistability with nonlinear interactions between contour trajectories, which similarly depends on the PT broken configuration of the system.

XAI in geographic analysis of innovation: Evaluating proximity factors in the innovation networks of Chinese technology companies through web-based data

This research explores the nonlinear interactions among multidimensional proximities, including geographical, cognitive, organizational, institutional, social, and technological aspects, and their impact on innovation within networks of over three million technology firms in China. Utilizing an innovative combination of web-based hyperlink and textual data analysis, supplemented by patent information, we delve into how these proximity dimensions influence corporate innovation capabilities. Our methodology integrates text-based deep learning techniques and employs the XGBoost model along with the SHapley Additive exPlanations (SHAP) algorithm and partial dependence plots to uncover the nuanced effects of proximity on innovation. The findings reveal that while geographical distance often correlates with larger cognitive and organizational proximities, underdeveloped regions exhibit stronger technological, institutional, and social proximities compared to their developed counterparts. The study further identifies social structure and technological differences as pivotal factors impacting collaborative innovation, with both positive and negative effects fluctuating alongside changes in proximity dimensions. Notably, we uncover that geographical proximity has a pronounced boundary effect on innovation, highlighting the critical role of spatial considerations in the digital age of innovation networks. This research contributes to the understanding of urban innovation dynamics and offers valuable insights for policymakers and urban planners aiming to foster innovation ecosystems.

Prediction of bike-sharing station demand using explainable artificial intelligence

Bike-sharing systems have grown in popularity in metropolitan areas, providing a handy and environmentally friendly transportation choice for commuters and visitors alike. As demand for bike-sharing programs grows, efficient capacity planning becomes critical to ensuring good user experience and system sustainability in terms of demand. The random forest model was used in this study to predict bike-sharing station demand and is considered a strong ensemble learning approach that can successfully capture complicated nonlinear correlations and interactions between input variables. This study employed data from the Smart Location Database (SLD) to test the model accuracy in estimating station demand and used a form of explainable artificial intelligence (XAI) function to further understand machine learning (ML) prediction outcomes owing to the blackbox tendencies of ML models. Vehicle Miles of Travel (VMT) and Greenhouse Gas (GHG) emissions were the most important features in predicting docking station demand individually but not holistically based on the datasets. The percentage of zero-car households, gross residential density, road network density, aggregate frequency of transit service, and gross activity density were found to have a moderate influence on the prediction model. Further, there may be a better prediction model generating sensible results for every type of explanatory variable, but their contributions are minimum to the prediction outcome. By measuring each feature's contribution to demand prediction in feature engineering, bike-sharing operators can acquire a better understanding of the bike-sharing station capacity and forecast future demands during planning. At the same time, ML models will need further assessment before a holistic conclusion.

Nonlinear seismic assessment for nuclear power plants with structural-geotechnical hybrid isolation strategy

Improving the seismic resilience of nuclear power plants (NPP) to withstand super-strong earthquakes is critical for ensuring nuclear safety. Seismic isolation can effectively enhance the structural seismic margins and has significant potential for development in NPPs. Although three-dimensional (3D) base isolation can effectively isolate tri-directional ground motion, it also faces challenges such as the limitations of horizontal and vertical displacements of the isolation layer under super-strong earthquakes. In this paper, a novel hybrid passive control strategy that combines 3D base isolation with geotechnical seismic isolation (GSI) systems is proposed. Furthermore, a nonlinear soil-structure interaction analysis method is developed and the program is validated using numerical examples. Finally, a nonlinear dynamic analysis of a large-scale nuclear island building at a non-bedrock site is conducted to investigate the seismic mitigation effects of a hybrid isolation system under super-strong earthquakes. The research findings demonstrate a significant improvement in the isolation effectiveness of the proposed strategy compared to 3D base isolation, which further reduces the tri-directional seismic responses of NPPs while also exhibits the ability to control the deformation in the isolation layer. The proposed hybrid isolation system can significantly improve the ability of NPPs to withstand super-strong earthquakes and provide support for the hybrid seismic isolation design of NPPs.

Forecasting the architecture billings index (ABI) using machine learning predictive models

PurposeThis research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects.Design/methodology/approachThe architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets.FindingsThe experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements.Practical implicationsThe architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bids more strategically to avoid a bid loss or profit loss.Originality/valueThe ABI data have been forecasted and modeled using linear time series models. However, linear time series models often fail to capture nonlinear patterns, interactions, and dependencies among variables, which can be handled by machine learning models in a more flexible manner. Despite the strength of machine learning models to capture nonlinear patterns and relationships between variables, the applicability and forecasting performance of multivariate machine learning models have not been investigated for ABI forecasting problems. This research first attempted to forecast ABI data for different time horizons using multivariate machine learning predictive models using different macroeconomic and construction market variables.

Advanced Modeling of Electro‐Optic Sampling: Nonlinear Vectoral‐Field Solutions to Maxwell's Equations

AbstractA comprehensive modeling approach for elucidating the intricacies of electro‐optic (EO) sampling is presented, which fully encapsulates the EO sampling process by accounting for all linear and second‐order nonlinear optical effects. The developed approach implements a multi‐step procedure, involving a vectorial‐field modeling of the second‐order nonlinear interactions occurring within the EO crystal, followed by a theoretical evaluation of the subsequent optical EO sampling components. To assess the efficacy of the approach, it is used for the sampling of terahertz electric fields (<1–>60 THz) within the bulk crystals of ZnTe and LiNbO3, as well as a ZnTe‐based waveguide. Nonetheless, this versatile method enables the investigation of EO sampling within any crystal incorporated within any geometry. This model provides a powerful tool for the in‐depth exploration of EO sampling, paving the way for advancements in various applications.

Dynamical Systems Characterisation and Reduced Order Modelling of Thermoacoustics in a Lean Direct Injection (LDI) Hydrogen Combustor

Abstract In this study, the thermoacoustic instabilities of partially premixed hydrogen flames in a lean direct injection (LDI) multi-cluster combustor are characterised using dynamical systems theory. The combustor is operated at a range of bulk velocities (30 - 90 m/s) and equivalence ratios(0.2-0.6), and time-resolved pressure oscillations and integrated OH* chemiluminescence measurements were taken. The thermoacoustic system reveals a variety of dynamical states in pressure such as period-1 Limit Cycle Oscillation (LCO) with a single characteristic frequency, period-2 LCO with two characteristic frequencies, intermittent, quasi-periodic and chaotic states as either bulk velocity or equivalence ratio is varied. At a bulk velocity of 30 m/s, as the equivalence ratio is gradually decreased from 0.6 to 0.2, the dynamical behaviour follows a sequence from an intermittent state to a period-1 LCO, then to a quasi-periodic state, and eventually reaches a chaotic state. As the equivalence ratio is decreased for a bulk velocity of 60 m/s, the pressure oscillations evolve from a period-2 LCO to quasi-periodic state before flame blows off. The emergence of period-2 and quasi-periodic states indicates the presence of strong non-linear interactions among the cavity acoustic modes. The emergence of period-2 and quasi-periodic states indicates strong non-linear interactions among the cavity acoustic modes. These modes and their spatial behaviour are investigated using a Helmholtz solver, showing that hydrogen flames can excite a wide range of cavity acoustic modes

Three-wave mixing experiments in indium–tin–oxide thin-films with no phase matching

Abstract One challenge of using nonlinear optical phenomena for practical applications is the need to perform phase-matching. Recently, epsilon-near-zero materials have been shown to demonstrate strong optical nonlinearities, in addition to their other unique properties. As suggested by their name, the permittivity of the material is close to zero for a certain wavelength range. We demonstrate that this small permittivity allows for efficient three-wave mixing interactions to take place in indium–tin–oxide thin films without the need for phase matching the pump and signal beams. The efficiency of the second-order nonlinear interactions is characterized, and cascaded three-wave mixing is demonstrated.

Travelling waves in beam-like structures submerged in water

There are many examples in nature of travelling waves used for propulsion purposes, e.g., micro-organisms and sea creatures. Structural travelling waves can be used to induce momentum in a surrounding media creating a net propelling force. Recent research has tried to capture this interaction in engineering devices. Nonetheless, some challenges remain to fully exploit this phenomenon so that travelling-waves propelled devices can be optimally designed. One such challenge is that the interaction between the structure and the surrounding fluid heavily influences the amplitude of the waves and how they travel through the structure. This paper proposes a systematic qualitative and quantitative analysis of travelling waves in a slender cantilever beam submerged in water. The novelty of this work is demonstrated through two key aspects: The application of the Euler-Bernoulli beam equation combined with the Galerkin approximation, enabling a deeper understanding of how travelling waves form at resonant frequencies rather than non-resonant ones; and An analytical approach using a Galerkin approximation to characterise the nonlinear fluid-structure interaction, followed by linearisation for a comprehensive parametric study of the problem. In this investigation, the contributions of the first five vibration modes are considered in relation to the travelling waves observed near the resonant peaks. Experimental tests validate the analytical results and assess the accuracy of the proposed models. The results demonstrate that the model presented effectively characterises the travelling waves, making a suitable tool for the design of travelling-wave propelled devices.

Second Harmonic Generation from exciton-polaritons: Strong coupling between monolayer WS[formula omitted] and multilayer WSe[formula omitted] metasurfaces Quasibound states in the continuum

The second harmonic generation (SHG) from exciton-polaritons, produced by the strong coupling between quantum emitters and cavity photons, can provide a unique insight for controlling nonlinear responses on the nanoscale. Achieving SHG generally depends on phase matching, limited by nonlinear materials and nanostructure fabrication. Here, we demonstrate a novel way to obtain tunable SHG from van der Waals metasurfaces composed of nanostructured multilayer WSe2 strong coupling with monolayer WS2. By breaking symmetry via varying the metasurface unit tilted angle and period, quasibound state in the continuum (q-BIC) mode resonances energy is tuned across the monolayer WS2 exciton resonance energy, enabling anticrossing behavior of strong coupling in both reflection spectrum and SHG spectrum, which means that SHG was successfully observed by strong coupling. Furthermore, the SHG spectrum can be tuned by changing the armchair direction of the monolayer WS2 and pump polarization direction. Moreover, the mechanism of exciton-polaritonic SHG is investigated by a coupled nonlinear oscillator model, which reveals the SHG origin from exciton-polaritons, providing a new rule for fine-tuning the SHG. Our work provides a new strategy and additional freedom in the nonlinear light-matter interaction and coherent control of the SHG in integrated photonic devices.

A competing risks machine learning study of neutron dose, fractionation, age, and sex effects on mortality in 21,000 mice

This study explores the impact of densely-ionizing radiation on non-cancer and cancer diseases, focusing on dose, fractionation, age, and sex effects. Using historical mortality data from approximately 21,000 mice exposed to fission neutrons, we employed random survival forest (RSF), a powerful machine learning algorithm accommodating nonlinear dependencies and interactions, treating cancer and non-cancer outcomes as competing risks. Unlike traditional parametric models, RSF avoids strict assumptions and captures complex data relationships through decision tree ensembles. SHAP (SHapley Additive exPlanations) values and variable importance scores were employed for interpretation. The findings revealed clear dose–response trends, with cancer being the predominant cause of mortality. SHAP value dose–response shapes differed, showing saturation for cancer hazard at high doses (> 2 Gy) and a more linear pattern at lower doses. Non-cancer responses remained more linear throughout the entire dose range. There was a potential inverse dose rate effect for cancer, while the evidence for non-cancer was less conclusive. Sex and age effects were less pronounced. This investigation, utilizing machine learning, enhances our understanding of the patterns of non-cancer and cancer mortality induced by densely-ionizing radiations, emphasizing the importance of such approaches in radiation research, including space travel and radioprotection.

High-harmonic spin-orbit angular momentum generation in crystalline solids preserving multiscale dynamical symmetry.

Symmetries essentially provide conservation rules in nonlinear light-matter interactions and facilitate control and understanding of photon conversion processes or electron dynamics. Since anisotropic solids have rich symmetries, they are strong candidates for controlling both optical micro- and macroscale structures, namely, spin angular momentum (circular polarization) and orbital angular momentum (spiral wavefront), respectively. Here, we show structured high-harmonic generation linked to the anisotropic symmetry of a solid. By strategically preserving a dynamical symmetry arising from the spin-orbit interaction of light, we generate multiple orbital angular momentum states in high-order harmonics. The experimental results exhibit the total angular momentum conservation rule of light even in the extreme nonlinear region, which is evidence that the mechanism originates from a dynamical symmetry. Our study provides a deeper understanding of multiscale nonlinear optical phenomena and a general guideline for using electronic structures to control structured light, such as through Floquet engineering.

Giant nonlinear optical wave mixing in a van der Waals correlated insulator.

Optical nonlinearities are one of the most fascinating properties of two-dimensional (2D) materials. While tremendous efforts have been made to find and optimize the second-order optical nonlinearity in enormous 2D materials, opportunities to explore higher-order ones are elusive because of the much lower efficiency. Here, we report the giant high odd-order optical nonlinearities in centrosymmetric correlated van der Waals insulator manganese phosphorus triselenide. When illuminated by two near-infrared femtosecond lasers, the sample generates a series of profound four- and six-wave mixing outputs. The near-infrared third-order nonlinear susceptibility reaches near the highest record values of 2D materials. Comparative measurements to other prototypical nonlinear optical materials [lithium niobate, gallium(II) selenide, and tungsten disulfide] reveal its extraordinary wave mixing efficiency. The wave mixing processes are further used for nonlinear optical waveguide with multicolor emission. Our work highlights the promising prospect for future research of the nonlinear light-matter interactions in the correlated 2D system and for potential nonlinear photonic applications.