Inverse Approach Research Articles

Seismic deconvolution based on SNR-weighted stacking

We propose a seismic deconvolution algorithm based on signal-to-noise ratio (SNR)-weighted stacking. First, we applied the time-frequency analysis to decompose the common midpoint (CMP) or common reflection point (CRP) gather. It is considered that there are differences in noise between different traces in the frequency domain profile. Then, we employ an SNR-weighted stacking strategy in each frequency-domain profile. Finally, we construct the resolution-enhancement amplitude spectrum, apply a linear inversion approach to calculate the overall weights of each frequency-domain profile, perform weighted stacking between the frequency-domain profiles, and obtain the resolution-enhancement post-stack data. The frequency decomposition of the pre-stack gather is efficiently utilized for the primary data of the proposed method. The SNR-weighted stacking adaptively selects the components with high SNR at each frequency. In addition, the resolution-oriented stacking weights are determined by the linear inversion approach. This algorithm can be adaptively weighted by considering the SNR of each frequency component, which can effectively overcome the problem of low SNR in spectral simulation and obtain high-resolution seismic data. The algorithm does not include wavelet extraction and regularization; thus, the resolution-enhancement data is more reliable. Applications to synthetic and field seismic datasets demonstrate that the proposed resolution-enhancement methodology is of great benefit to the succeeding structural interpretation and thin-bed identification.

Multimodal surface wave inversion with automatic differentiation

SUMMARY Investigating subsurface shear wave velocity (vs) structures using surface wave dispersion data involves minimizing a misfit function that is commonly solved through gradient-based optimization. Sensitivity kernels for model updates are commonly estimated using numerical differentiation, variational methods or implicit functions which however, may involve numerical instability and computational challenges when dealing with complex velocity models and large data sets. In this study, we propose a novel surface wave inversion framework in which error-free gradients are calculated by automatic differentiation (AD) and forward modelling is implemented by convenient computational graphs in the state-of-the-art deep learning framework. The AD-based inversion approach is first validated using two synthetic data sets. Then, the subsurface structures at three distinct locations, namely the Great Plains and the Long Beach in the US and Tong Zhou in China, are also derived using this method with seismic ambient noise data, which show nice consistency with those obtained using traditional methods. With the significantly improved computational efficiency, a great number of initial models can be inverted simultaneously to mitigate the impact of local minima and to estimate the uncertainty in the invert models. We have developed a new surface wave inversion package named ADsurf based on automatic differentiation and computational graphs in the deep learning framework, and its computational efficiency is also compared with the traditional finite-difference-based gradient estimation approach. While a great number of intriguing studies on the geophysical inverse problems have been conducted recently using deep learning for end-to-end mapping, the use of AD provided in the in the deep learning frameworks to assist and expedite the gradient computations are still underexploited in geophysics. Thus, it is expected that various geophysical inverse problems in many different areas beyond the surface wave inversion can also be tackled with this new paradigm in the future.

Multifunctional tri-layer aramid nanofiber composite separators for high-energy-density lithium-sulfur batteries

Lithium-sulfur (Li-S) battery has attracted much attention due to their high theoretical energy density. However, severe shuttling effect, sluggish reaction kinetics and uncontrolled dendrite formation of Li anode greatly deter their practical applications. Different to conventional separator engineering strategies that are always focused on surface coating of functional interlayer on commercial polyolefin separators, herein, we proposed a structural engineering tactic by rationally tuning the porous structure and composition of the p-aramid nanofiber (ANF) composite separator to address simultaneously the above problems. A two-step film-casting process combined with a phase inversion approach was developed to fabricate a multifunctional tri-layer ANF composite separator. This innovative separator was especially engineered to comprise a conductive and catalytic NiFe2O4 modified multi-walled carbon nanotubes/ANF layer for rapid and efficient conversion of lithium polysulfides, a microporous ANF layer for fast ion transport, and a dense nanoporous ANF layer for lithium dendrite inhibition. This tri-layer ANF-based separator possessed high porosity, excellent thermal stability, and superb electrolyte wettability. Batteries with the tri-layer ANF- based separator exhibited outstanding cyclic stability with high capacity of >3 mAh cm−2 at high sulfur loading (4 mg cm−2) and high current density (4 mA cm−2). This work opens up new opportunities for design of functional separators based on high performance polymers using the structural engineering strategy.

Design of second-order phoxonic topological insulators with customized bandgaps

Second-order phononic and photonic topological insulators hosting topologically protected edge and corner states provide opportunities for the robust manipulation of elastic/acoustic and electromagnetic (EM) waves, respectively. Despite their potential, the development of second-order phoxonic topological insulators, which combine functionalities of second-order phononic and photonic topological insulators, remains largely unexplored. Herein, we design second-order phoxonic topological insulators (SPTI) with customized bandgaps. An inverse design approach is proposed to engineer phoxonic crystals (PCs) featuring customized odd-order phononic and photonic bandgaps simultaneously. Topological phase transition is induced by translating the primitive unit cell (UC) of the inverse-designed PC with half of the lattice constant horizontally and vertically. Thereafter, the SPTI is constructed by juxtaposing the primitive and translated UCs to form a corner between them. Multiple SPTIs, capable of manipulating both acoustic and EM waves, as well as those governing elastic and EM waves, are created. Our work paves the way for customized SPTIs with tailored bandgaps to support diverse phononic and photonic corner states. Meanwhile, the designed SPTIs also provide a platform to design the higher-order topological optomechanical devices.

Comparing machine learning and inverse modeling approaches for the source term estimation

Comparing machine learning and inverse modeling approaches for the source term estimation

Increased risk of preterm birth due to heat exposure during pregnancy: Exploring the mechanism of fetal physiology

BackgroundHeat exposure during pregnancy can increase the risk of preterm birth (PTB) through a range of potential mechanisms including pregnancy complications, hormone secretion and infections. However, current research mainly focuses on the effect of heat exposure on pathophysiological pathways of pregnant women, but ignore that maternal heat exposure can also cause physiological changes to the fetus, which will affect the risk of PTB. ObjectiveIn this study, we aimed to explore the mediating role of fetal heart rate (FHR) in the relationship between maternal heat exposure and PTB incidence. MethodsWe assigned heat exposure to a multi-center birth cohort in China during 2015–2018, which included all 162,407 singleton live births with several times FHR measurements during the second and third trimesters. We examined the associations between heat exposure, FHR and PTB in the entire pregnancy, each trimester and the last gestational month. The inverse odds ratio-weighted approach applied to the Cox regression was used to identify the mediation effect of heat exposure on PTB and its clinical subtypes via FHR. FindingsExposure to heat significantly increased the risk of PTB during the third trimester and the entire pregnancy, hazard ratios and 95 % CIs were 1.266 (1.161, 1.379) and 1.328 (1.218, 1.447). Heat exposure during the third trimester and entire pregnancy increased FHR in the third trimester by 0.24 bpm and 0.14 bpm. The proportion of heat exposure mediated by FHR elevation on PTB and its subtype ranged from 3.68 % to 24.06 %, with the significant mediation effect found for both medically indicated PTB and spontaneous PTB. ConclusionsThis study suggests that heat exposure during pregnancy has an important impact on fetal health, and FHR, as a surrogate marker of fetal physiology, may mediate the increased risk of PTB caused by extreme heat. Monitoring and managing physiological changes in the fetus would constitute a promising avenue to reduce adverse birth outcomes associated with maternal heat exposure.

Coarse-grained simulation of thermal conductivity of boron nitride/epoxy composites based on DPD and SPH method

In this study, thermal conductivity (TC) of BN/polymer composites is investigated utilizing dissipative particle dynamics (DPD) and smoothed-particle hydrodynamics (SPH) methods by establishing the coarse-grained (CG) models of boron nitride nanotube (BNNT) and boron nitride nanosheet (BNNS). The Iterative Boltzmann inversion (IBI) approach is employed to calculate the parameters of the potential function of the hexagonal boron nitride (h-BN) coarse grain model for DPD method. Mesoscopic simulations on the TC of BNNT/EP, BNNS/EP, and BNNT/BNNS/EP composites are performed using SPH and DPD simulations to investigate the effects of nanofiller ratio, aspect ratio, size, and orientation on TC. The CG models established for the BNNT and BNNS structures are proven to be efficient in DPD and SPH simulations for predicting the TC of h-BN/polymer composites. Furthermore, it is demonstrated that the TC of BNNT/BNNS/EP composites is superior to that of BNNT/EP and BNNS/EP composites at the same filler ratio due to the synergistic effect of BNNT and BNNS.

Stochastic inversion of fracture networks using the reversible jump Markov chain Monte Carlo algorithm

Characterization of fracture networks is essential in the production optimization or storage calculation for enhanced geothermal systems or geologic carbon storage. A novel inversion approach is proposed for estimating the fracture networks in this research. The discrete fracture network technique is adopted to probabilistically describe various fracture parameters such as trace length, midpoint position or azimuthal angle. The reversible jump Markov chain Monte Carlo algorithm is applied to explore the target posterior distribution of model parameters of differing dimensionality, in which the number of fractures is assumed to be unknown. More specifically, the birth-death strategy is utilized to perturb the fracture number iteratively in the sampling process. The proposed methodology is applied with two different types of observational datasets, namely the head records from steady-state flow simulation and the acoustic impedance obtained from seismic inversion. The sampling results can successfully recover the fracture geometry in the observed domain, and the number of fractures in the system can be retrieved as well. Benchmarked on multiple Markov chain trials, the technique of parallel tempering can greatly improve the convergence efficiency and increase the diversity of sampled posterior models, through the random swapping of model states across the whole temperature ladder.

3D rock physics template-based probabilistic estimation of tight sandstone reservoir properties

Quantitative prediction of reservoir properties (e.g., gas saturation, porosity, and shale content) of tight reservoirs is of great significance for resource evaluation and well placements. However, the complex pore structures, poor pore connectivity, and uneven fluid distribution of tight sandstone reservoirs make the correlation between reservoir parameters and elastic properties more complicated and thus pose a major challenge in seismic reservoir characterization. We have developed a partially connected double porosity model to calculate elastic properties by considering the pore structure and connectivity, and to analyze these factors' influences on the elastic behaviors of tight sandstone reservoirs. The modeling results suggest that the bulk modulus is likely to be affected by the pore connectivity coefficient, while the shear modulus is sensitive to the volumetric fraction of stiff pores. By comparing the model predictions with the acoustic measurements of the dry and saturated quartz sandstone samples, the volumetric fraction of stiff pores and the pore connectivity coefficient can be determined. Based on the calibrated model, we have constructed a 3D rock physics template that accounts for the reservoir properties’ impacts on the P-wave impedance, S-wave impedance, and density. The template combined with Bayesian inverse theory is used to quantify gas saturation, porosity, clay content, and their corresponding uncertainties from elastic parameters. The application of well-log and seismic data demonstrates that our 3D rock physics template-based probabilistic inversion approach performs well in predicting the spatial distribution of high-quality tight sandstone reservoirs in southwestern China.

Urban methane emission monitoring across North America using TROPOMI data: an analytical inversion approach

Monitoring methane emissions is crucial in mitigating climate change as it has a relatively short atmospheric lifetime of about 12 years and a significant radiative forcing impact. To measure the impact of methane-controlling policies and techniques, a deep understanding of methane emissions is of great importance. Remote sensing offers scalable approaches for monitoring methane emissions at various scales, from point-source high-resolution monitoring to regional and global estimates. The TROPOMI satellite instrument provides daily XCH4 data globally, offering the opportunity to monitor methane at a moderate spatial resolution with an acceptable level of sensitivity. To infer emissions from TROPOMI data, we used the prior emission estimates from global and national inventories and the GEOS-Chem chemical transport model to simulate atmospheric methane along with actual observations of TROPOMI. In this study, methane emissions from Toronto, Montreal, New York, Los Angeles, Houston, and Mexico City have been estimated using the analytical solution of Bayesian inversion using the cloud-based Integrated Methane Inversion (IMI) framework. Using the result from ensemble inversions, and city boundaries, the average total emissions were as follows: Toronto 230.52 Gg a−1, Montreal 111.54 Gg a−1, New York 144.38 Gg a−1, Los Angeles 207.03 Gg a−1, Houston 650.16 Gg a−1, and Mexico City 280.81 Gg a−1. The resulting gridded scale factors ranged from 0.22 to 6.2, implying methane prior emission underestimations in most of these cities. As such, this study underscores the key role of remote sensing in accurately assessing urban methane emissions, informing essential climate mitigation efforts.

Computational simulation of impact perforation of polymeric-foam core sandwiched composites with different skin–face configurations

The normal and oblique impact perforation responses of composite sandwich panels based on a Rohacell polymeric-foam core are numerically investigated at high impact energies (>60 J). A cylindrical form factor with a diameter of 140 mm and a thickness of 15 mm is selected for the sandwich specimens. Four different stacking sequences of 1 mm carbon/epoxy face sheets are considered (i.e., quasi-isotropic, cross-ply, angle-ply, and unidirectional stacking). A computational model was constructed using LS-DYNA finite element software and an inverse perforation testing scheme adapted with a split Hopkinson bar and confirmed by comparing these results with those obtained using the free shooting projectile-target testing schemes published in the literature. The effects of impact energy, failure modes, impact angles, and damage key parameters are analyzed. The results reveal the contact force versus displacement curves are highly influenced by the impact energy increases. The stacking sequence of the face sheets does not influence the energy absorption capacity. However, the maximum absorbed energy increases with an increasing impact angle up to 20°. Using Hopkinson bars in conjunction with the virtual inverse perforation testing approach is effective for examining the response of sandwich composites at high impact energies.

AI-assisted inverse design of sequence-ordered high intrinsic thermal conductivity polymers

Artificial intelligence (AI) promotes the polymer design paradigm from a traditional trial-and-error approach to a data-driven style. Achieving high thermal conductivity (TC) for intrinsic polymers is urgent because of their importance in the thermal management of many industrial applications such as microelectronic devices and integrated circuits. In this work, we have proposed a robust AI-assisted workflow for the inverse design of high TC polymers. By using 1144 polymers with known computational TCs, we construct a surrogate deep neural network model for TC prediction and extract a polymer-unit library with 32 sequences. Two state-of-the-art multi-objective optimization algorithms of unified non-dominated sorting genetic algorithm III (U-NSGA-III) and q-noisy expected hypervolume improvement (qNEHVI) are employed for sequence-ordered polymer design with both high TC and synthetic possibility. For triblock polymer design, the result indicates that qNHEVI is capable of exploring a diversity of optimal polymers at the Pareto front, but the uncertainty in Quasi-Monte Carlo sampling makes the trials costly. The performance of U-NSGA-III is affected by the initial random structures and usually falls into a locally optimal solution, but it takes fewer attempts with lower costs. 20 parallel U-NSGA-III runs are conducted to design the pentablock polymers with high TC, and half of the candidates among 1921 generated polymers achieve the targets (TC > 0.4 W m−1K−1 and SA < 3.0). Ultimately, we check the TC of 50 promising polymers through molecular dynamics simulations and reveal the intrinsic connections between microstructures and TCs. Our developed AI-assisted inverse design approach for polymers is flexible and universal, and can be extended to the design of polymers with other target properties.

Terahertz metalens for generating multi-polarized focal points and images with uniform intensity distributions.

Metasurfaces have provided a flexible platform for designing ultracompact metalenses with unusual functionalities. However, traditional multi-foci metalenses are limited to generating circularly polarized (CP) or linearly polarized (LP) focal points, and the intensity distributions are always inhomogeneous/chaotical between the multiple focal points. Here, an inverse design approach is proposed to optimize the in-plane orientation of each meta-atom in a terahertz (THz) multi-foci metalens that can generate multi-polarized focal points with nearly uniform intensity distributions. As a proof-of-principle example, we numerically and experimentally demonstrate an inversely designed metalens for simultaneously generating multiple CP- and LP-based focal points with homogeneous intensity distributions, leading to a multi-polarized image (rather than the holography). Furthermore, the multi-channel and multi-polarized images consisting of multiple focal points with homogeneous intensity distributions are also numerically demonstrated. The unique approach for inversely designing multi-foci metalens that can generate multi-polarized focal points and images with uniform intensity distributions will enable potential applications in imaging and sensing.

Design, tuning, and blackbox optimization of laser systems

Chirped pulse amplification (CPA) and subsequent nonlinear optical (NLO) systems constitute the backbone of myriad advancements in semiconductor manufacturing, communications, biology, defense, and beyond. Accurately and efficiently modeling CPA+NLO-based laser systems is challenging because of the complex coupled processes and diverse simulation frameworks. Our modular start-to-end model unlocks the potential for exciting new optimization and inverse design approaches reliant on data-driven machine learning methods, providing a means to create tailored CPA+NLO systems unattainable with current models. To demonstrate this new, to our knowledge, technical capability, we present a study on the LCLS-II photo-injector laser, representative of a high-power and spectro-temporally non-trivial CPA+NLO system.

Inferring the tree regeneration niche from inventory data using a dynamic forest model

Abstract. The regeneration niche of trees is governed by many processes and factors that are challenging to determine. Besides a species's geographic distribution, which determines if seeds are available, a myriad of local processes in forest ecosystems (e.g., competition and pathogens) exert influences on tree regeneration. Consequently, the representation of tree regeneration in dynamic forest models is a notoriously complicated process which often involves many subprocesses that are often data deficient. The ForClim forest gap model solved this problem by linking species traits to regeneration properties. However, this regeneration module was never validated with large-scale data. Here, we compare this trait-based approach with an inverse calibration approach where we estimate regeneration parameters directly from a large dataset of unmanaged European forests. The inverse calibration was done using Bayesian inference, estimating shade and drought tolerance as well as the temperature requirements for 11 common tree species along with the intensity of regeneration (i.e., the maximum regeneration rate). We find that the parameters determining the species' light niche (i.e., light requirements) are similar for the trait-based and calibrated values for both model variants, but only a more complex model variant that included competition between recruits leads to plausible estimates of the drought niche. The trait-derived temperature niche did not match to the estimates from either model variant using inverse calibration. The parameter estimates differed between the complex and the simple model, with the estimates for the complex model being closer to the trait-based parameters. In both model variants, the calibration strongly changed the parameters that determine regeneration intensity compared to the default. We conclude that the regeneration niche of trees can be recovered from a large forestry dataset in terms of the stand-level parameters light availability and regeneration intensity, while abiotic drivers (temperature and drought) are more elusive. The higher performance (better fit to hold out) of the inversely calibrated models underpins the importance of informing dynamic models by real-world observations. Future research should focus on even greater environmental coverage of observations of demographic processes in unmanaged forests to verify our findings at species range limits under extreme climatic conditions.

One‐way waveform inversion: Real marine data application

AbstractThe reflection waveform inversion is a powerful technique to build a large‐scale velocity model of the subsurface by fitting the reflected recorded seismic waves. The reflection waveform inversion is designed based on the pillar concept of model and data‐scale separation. Therefore, its success is related to the ability of its forward modelling engine to separate reflected events distinctly from other propagation modes (diving waves, multiples, etc.). However, the standard Born modelling based on the two‐way wave equation may generate internal multiples in case of an insufficient smooth background model. These internal multiples may lead to a distorted sensitivity kernel, which adds more non‐linearity to the inverse problem. In addition, simulating the wave equation using two‐way propagators is still an overburden step of the algorithm especially in large three‐dimensional real surveys. In this proposal, we introduce an alternative to the two‐way wave equation by using a one‐way approach for the reflection waveform inversion. The Born modelling based on one‐way propagators significantly reduces the computational cost and I think it should be allows to relax the smooth background velocity model assumption by restricting the forward modelling to primary reflected waves. After a brief theoretical description of the one‐way waveform inversion, we present an application of the algorithm on the real marine dataset to review its promises and pitfalls. Our approach produces an acceptable large‐scale velocity model whose accuracy is confirmed by the migrated image and the offset gathers.

ShaVi-1.0: An interface enabled open source 2D acoustic full waveform inversion package

Full waveform inversion (FWI) is an approach of seismic data inversion that employs data-fitting techniques to produce a detailed image of the subsurface. The computational expense and complex mathematics involved in implementing FWI make it challenging. To address this issue, we have developed an interface enabled 2D acoustic time domain FWI package: ShaVi-1.0 (“Sha” refers to “Shabda”, a Sanskrit word meaning “sound”, and “Vi” stands for “Viparita”, representing “inversion”, the package version is labeled as 1.0). That utilizes shots parallelization on the OpenMP API to deal with computational expenses and a graphical user interface (GUI) for its easy implementation. This allows the FWI to run on standalone workstations. The base code of FWI is written in “C” language with a modular design, enabling users to modify the modules as per their requirements. The GUI, which is developed in Python, includes features such as real-time plotting and process status updates, along with message and warning boxes to ensure smooth inversion flow. We add a toolbox to the GUI’s menu bar along with the help, which users can use to generate acquisition geometry and plot source-receiver locations. The toolbar integrated into the software provides multiple other functionalities to enhance the ease of implementation. We perform testing on the benchmark models such as SEG/EAGE Overthrust and Foothill model. A comparative study is also performed to assess its efficiency relative to CSIM-KAUST and SWIT-1.0 codes. The assessment is based on two key parameters: mean absolute percentage error (MAPE and computational time. The developed GUI not only simplifies the implementation process but also enables real-time analysis of results.

Forward and inversion approach for direct current resistivity based on an unstructured mesh and its application to tunnel engineering

AbstractThe accurate identification of water‐bearing structures is urgently required for the safe construction of tunnel engineering. Currently, the direct current resistivity method is an effective method for detecting water‐bearing structures in tunnels. In the advanced detection of the direct current resistivity based on the finite element method, the traditional hexahedron mesh performs poorly for the discretization of models of complex tunnel structure sections such as horseshoe‐shaped and round sections. Therefore, this study adopts unstructured grid generation technology combining tetrahedra and hexahedra to achieve more accurate modelling of complex structures, such as round and horseshoe‐shaped sections, and establishes a forward modelling method of the direct current resistivity in tunnels based on an unstructured mesh. The maximum error between the numerical simulation and theoretical results for an infinite tabular body in full space is less than 0.8%. It is more complicated to calculate the sensitivity matrix and model constraint term for the inversion region containing two types of grid than for one. For this purpose, the sensitivity matrix of different types of grid areas is calculated, a model constraint term based on the dual constraints of volume and distance is constructed, and finally, a partitioned domain‐weighted least‐squares inversion method based on an unstructured mesh is proposed. Synthetic examples of typical water‐bearing structures are analysed, and the results show that the proposed forward and inverse methods of the direct current resistivity in tunnels based on an unstructured mesh can effectively capture the position and morphology of the water‐bearing structure. Finally, an on‐site application was conducted in the Yellow River Diversion Project in central Shanxi. The proposed method could effectively identify the water body in front of the tunnel face and guide the on‐site construction of the project. These results can improve the interpretation of the direct current resistivity data in tunnels and play a positive role in promoting the use of the direct current resistivity method to prevent and control water‐inrush disasters in tunnels with complex structures.

Joint inversion of potential field data with adaptive unstructured tetrahedral mesh

Inverting potential field data presents a significant challenge due to its ill-posed nature, often leading to nonunique model solutions. Addressing this, our work focuses on developing a robust joint inversion method for potential field data, aiming to achieve more accurate density and magnetic susceptibility distributions. Unlike most previous work that used regular meshes, our approach adopts an adaptive, unstructured tetrahedral mesh, offering enhanced capabilities in handling the inverse problem of potential field methods. During inversion, the tetrahedral mesh is refined in response to the model update rate. We integrate a Gramian constraint into the objective function, allowing the enforcement of model similarity in terms of either the model parameters or their spatial gradients on an unstructured mesh. In addition, we use the moving least-squares method for gradient operator computation, which is essential for model regularization. Our model studies indicate that this method effectively inverts potential field data, yielding reliable subsurface density and magnetic susceptibility distributions. The joint inversion approach, compared with individual data set inversion, produces coherent geophysical models with enhanced correlations. Notably, it significantly mitigates the nonuniqueness problem, with the recovered anomaly locations aligning more closely with actual ground truths. Applying our methodology and algorithm to field data from the Ring of Fire area in Canada, the joint inversion process has generated comprehensive geophysical models with robust correlations, offering potential benefits for mineral exploration in the region.

Modification of extended Oxley’s predictive machining theory and determination of Johnson-Cook material model constants by inverse approach

The Johnson-Cook (J-C) flow stress model, renowned for its consideration of the combined effect of strain, strain-rate, and temperature on material property, forms the cornerstone of this research. Its simplicity and applicability in numerical simulation render it widely popular. The study addresses two primary objectives: first, to modify extended Oxley’s predictive machining theory by eliminating the iterative loop for calculating the tool-chip interface temperature, and second, to utilize an inverse approach for determining J-C material model constants specific to medium carbon steel. The modified approach successfully eliminates the iterative loop, thereby streamlining the determination process and effectively reducing calculation time by 25%–40%, thereby enhancing overall efficiency. Concurrently, the study employs an inverse approach to accurately determine the J-C constants (A, B, n, C, and m). Notably, the research avoids the need for expensive and time-consuming Split Hopkinson Pressure Bar (SHPB) tests, instead opting for low-cost axial tensile tests and numerical optimization techniques to obtain the J-C constants. Thus, this study provides a practical method for determining J-C constants, pivotal for accurate material representation in numerical simulations and metal cutting applications. Standard materials and machines are employed, ensuring ease of understanding for a wider audience. The predicted values for the J-C constants are in proximity to the experimental values obtained by using SHPB test. Validated findings, achieved with precision under specified metal cutting conditions, highlight the effectiveness and applicability of the proposed approach, providing valuable insights for optimizing machining processes and improving efficiency.