The human cornea is a thin tissue that adopts an aspherical shape as a result of a delicate balance among geometry, intraocular pressure, external loads, boundary conditions, and material mechanical properties, whose characterization is a key factor for the development of accurate patient-specific computational analysis [1] with the integration of material models.

Among available material models, the use of anisotropic hyperelastic models based on the integration of fiber is widely accepted. This assumption is supported by evidence from several studies [2] demonstrating that the collagen fibers contained in the lamellae of the corneal stroma are parallel within each lamella and vary across lamellae. These fibers show preferential nasal–temporal and superior–inferior orientations in the central cornea, which are maintained toward the limbal region, where collagen fibers adopt a preferential circumferential arrangement [3]. Loss of such preferential organization is associated with diseases such as keratoconus [4,5] in which the degradation of mechanical properties leads to corneal bulging and results in severe visual impairment [6].

The first anisotropic hyperelastic models for soft tissues evolved from the exponential formulation proposed by Fung [7], in which the strain-energy density function is expressed in terms of the components of the Green–Lagrange strain tensor without explicitly introducing fiber orientation. This approach captures the global nonlinear response of the tissue from a purely phenomenological perspective, but the parameters lack physical meaning [8]. Additionally, the large number of parameters hinders their identification and may lead to non-polyconvex solutions that compromise model stability [9,10].

The development of invariant-based models represented a significant advancement in incorporating the contribution of fibrillar tissue components and remains the most widely used approach [9,10]. In such models, the free energy is decomposed into an isotropic part, corresponding to the extracellular matrix, and an anisotropic part, dependent on invariants associated with fiber directions. The inclusion of invariants in corneal analysis I4=m0⋅Cm0 and I6=n0⋅Cn0 enables capturing the strain along the preferential directions of two collagen fiber families, with these invariants representing the square of fiber stretch.

Formulations inspired by the Holzapfel–Gasser–Ogden (HGO) model [10] have been successfully adapted to corneal biomechanics, where two principal orthogonally oriented fiber families define stromal anisotropy [11]. These models offer an optimal balance between mathematical simplicity, numerical stability, and predictive capability, and they have therefore become the methodological standard for comparing experimental data and computational simulations in corneal biomechanics [1]. Their main limitations stem from the assumption of perfectly aligned fibers, which may overestimate stiffness in regions where angular dispersion is significant [10], the need to work in a limited range of deformation [12], and the need to be adapted in case of compression analysis [13]; however, their modular structure allows extensions to incorporate dispersion when structural data are available.

More recent models incorporate fiber dispersion through statistical distributions of collagen orientations [14], providing the most faithful representation of corneal microarchitecture. This approach can be formulated using angular integration (AI), where macroscopic response is obtained from an angular distribution function (e.g., von Mises, beta, or Gaussian), or through the generalized structure tensor (GST), which summarizes dispersion into an effective tensor [15,16]. In the cornea, these models allow reproducing regional variations in mechanical behavior and the influence of fibrillar architecture in conditions such as keratoconus [6]. Nevertheless, they require histological or small-angle X-ray scattering (SAXS) data to parameterize dispersion [17] and entail the development of new methodologies for assessing collagen fiber organization [18]. Furthermore, their numerical resolution can be computationally demanding when angular integration is explicitly implemented.

In our previous study [19] a stepwise identification process was proposed to reduce coupling between isotropic and anisotropic components showing that multiple parameter combinations can reproduce the same experimental tests, leading to non-unique but physically plausible solutions showing that direct material’s parameter sets calculation based on single tests is an ill-posed problem [20] and isotropic and anisotropic effects may compensate each other during optimization due to the nonlinear material behavior [21] being necessary the implementation of parametric analysis [22]. Furthermore, the combination of different IOP with parameter sets can reproduce similar results [23].

This lack of uniqueness hinders comparison between studies and limits the comparison between biomechanical models, necessitating the establishment of a standardized protocol to compare the results from different investigations adapted to the physiological stress state of the cornea that works as membranes submitted to a biaxial state due to the intraocular pressure effect.

This study proposes a unified comparative framework for the calculation of material parameters by employing the Holzapfel–Gasser–Ogden (HGO) model due to mathematical simplicity and predictive capacity, and a novel protocol based on a strain threshold for sequential parameter identification. The establishment of a strain threshold can be useful to consider the influence of matrix and fibers contribution allowing at low strain level, where fibers contribution is negligible, the use of uniaxial and equi-biaxial tests results for isotropic parameters calculation considering test coupons cut in any direction whereas uniaxial and equi-biaxial tests results considering superior-inferior and nasal-temporal directions can be used to calculate the anisotropic parameter with strain values exceeding the threshold. The calculation is improved with the implementation of an optimization process based on weighted functions to increase the accuracy in the physiological intraocular pressure range [21], considering data tests based on strain rates close to physiological states [24].

The development of this novel methodology may contribute to the establishment of a robust and efficient common framework for the comparison and validation of experimental data that can be adapted to incorporate dispersion when structural data are available and the definition of different material types for in silico, patient-specific biomechanical cornea models.

In this research, a structural methodology is proposed to optimise the material parameters of corneal tissue through a multi-stage approach applied to the average equi-biaxial test curve obtained from the investigation of Hatami-Marbini and Emu [24] at a strain rate of 2 mm/min. The mentioned investigation was carried out with porcine corneas, and the results are extrapolable considering the similar behaviour to the human cornea in short cycles [25,26] and considering the difficulty in obtaining human corneas.

The optimization process employed to determine the material parameters of a Holzapfel-Gasser-Ogden (HGO) model is based on the introduction of a strain threshold to delineate the region in which the influence of collagen fibres may be considered negligible. Collagen fibres are the main load-bearing elements of the cornea, and when they are stretched, the cornea exhibits a stiffening process known as collagen fibre recruitment [27], where the fibres gradually begin to work, increasing the stiffness of the tissue. This process is related to fibers’ tortuosity [28,29]. The collagen fibers’ tortuosity is reduced with age, and this factor could explain the stiffness increment obtained in the inflation test of groups of human corneas with different ages [30]. Considering the results of the inflation test, Jan and Sigal [31] proposed a limit of 10 mmHg. Under this value, the effect of collagen fibrils can be neglected. This value is close to the value obtained by Foong et al. [27] considering a global eye model. Under the framework of a simplified shell theory [32], the cornea is idealized as a membrane with pinned boundary conditions. In this way, true strain (ε) can be estimated according to the following equation:(1)ε=IOP⋅R2⋅t⋅Ewhere IOP is the intraocular pressure, R the average corneal radius, t the average corneal thickness, and E the elastic modulus. A sensitivity analysis was conducted to assess the influence of key biomechanical and geometrical parameters, including intraocular pressure (IOP), mean corneal radius (R), average corneal thickness (t), and Young’s modulus (E). The corresponding parameter ranges are presented in the following Table 1.

Estimated physiological strain has been calculated for each parameter range. The results obtained can be observed in Fig. 1.

According to previous research work [39], collagen fibrillar recruitment in the cornea does not occur immediately upon loading but is preceded by a regime dominated by molecular-level deformation until approximately 2.8% strain, according to experimental observations. At values lower than this strain, most of the deformation is accommodated by molecular straightening associated with the helical arrangement of tropocollagen, whereas mechanisms such as fibrillar elongation and reorientation become more evident beyond this level. In this research work, a strain level of 2% (Fig. 1) has been considered for a significant pre-recruitment regime with the hypothesis that it lies within the range where the response remains predominantly governed by molecular mechanisms [39] while remaining below the transition region and the minimum estimated physiological strain level calculated of 2.45%.

Below the 2% strain threshold (λ = 1.02), the corneal response can be considered isotropic, and both strip tensile and biaxial tests can be used with test coupons cut in any direction, making either valid to estimate the isotropic parameters c1 and c2. In this strain range, the cornea can be modelled with a simpler material model considering only the isotropic contribution of the Mooney-Rivlin material model. However, in simulations at higher strain levels, the collagen fiber recruitment cannot be neglected, and the contribution of the anisotropic component must be incorporated. The anisotropic material components k1 and k2 that dominate the mechanical response of the corneal tissue at higher strain levels can be obtained from uniaxial and equi-biaxial tests carried out considering nasal-temporal and superior-inferior directions where there is a preferential fiber orientation.

In this research, as an application case, experimental curves acquired at a strain rate of 2% per minute are used throughout the calibration process. This strain rate is compatible with the daily variation of the intraocular pressure (IOP) and the associated strain fluctuations 0%–3% that affect the corneal shape. Parameter optimization was carried out with ad-hoc software developed with Python version 3.11.9 (Python Software Foundation). The parameters calculated have been applied, as an application case, to a virtual inflation test of an in silico patient-specific cornea model carried out with a finite element model developed with ANSYS 2023 R1 software (Swanson Analysis System Inc., Houston, PA, USA). The entire process is schematized in Figs. 2 and 3.

Mathematical Modelling

It is widely accepted that corneal tissue can be modelled as a hyperelastic material where the local deformation is represented by the deformation gradient F=∂x∂X, which relates the position of a material point in the deformed configuration x to its initial position X. The determinant of this deformation gradient, J=det(F), represents the volumetric change of a differential volume and takes a value of 1 for incompressible materials. In isothermal processes, it is common to use a strain energy density function ψ from which the Cauchy stress tensor σ can be derived. For a quasi-incompressible material, this relationship is given by the following expression, where the Lagrange multiplier and the isochoric component of the strain energy density ψ¯ is considered:(2)σ=−pI+2JF∂Ψ¯∂CFT

The Lagrange multiplier p is determined by imposing an incompressibility constraint. The second component considers the deviatoric component, where C=FTF is the right Cauchy-Green deformation tensor. The deviatoric component, which is expressed as a function of invariants, can be observed in the following equation. In this research, an HGO-Mooney-Rivlin constitutive model is considered. (3)ψdeviatoric=c1(I¯1−3)+c2(I¯2−3)+k12k2∑i=4,6(exp[k2(I¯i−3)2]−1)where the parameters c1 and c2 represent the isotropic behaviour of the material, and the parameters k1 and k2 define the anisotropic behaviour of the material.

The parameter k1 defines the stiffness influence of the fibers in the preferred direction analysed and will vary depending on the fibers’ concentration, while k2 is a dimensionless parameter that accounts for the nonlinear behaviour of the fibers at large strains.

The isotropic expression is defined using a Neo-Hookean model, where the invariant I1¯=trC¯ represents the trace of the right Cauchy-Green deformation tensor where C¯=F¯TF¯ with F¯=J−1/3F, the invariant I2¯ is defined as I2¯=12((tr(C¯))2−tr(C¯2)), while the invariants I4¯=m0C¯m0 and I6¯=n0C¯n0 are pseudo-invariants that represent the squared fiber stretches in the initial state. Defining the preferred fiber directions as m0=[1,0,0] and n0=[0,1,0].

Considering the proposed model, the stress of a uniaxial and biaxial tensile test depending on the stretch can be observed in Eqs. (4)–(7), respectively:

Uniaxial tensile test

As can be observed, the stress value depends on the stretch (λ) value reached and the material properties (c₁, c₂, k_1, and k₂) that can be calculated based on test results. In this research work, the following multistage process is proposed according to Fig. 4:

In step 1, experimental data from uniaxial and/or equi-biaxial tests are considered to calculate the isotropic parameters c1 and c2 in the low strain regime, under the strain threshold (λ = 1.02) and considering an iterative parameter optimization process based on the definition of a differential evolution algorithm. The mathematical formulation of the Cauchy stress tensor differs depending on the loading condition. In the uniaxial case, assuming deformation in the x-direction and zero lateral stresses (σy=σz=0), the Cauchy stress is given by:(8)σx=2c1(λ2−1λ)+2c2(λ−1λ2)

Whereas in the equi-biaxial case, assuming deformation in the x-direction and y-direction and zero stress in the z-direction, the Cauchy stress is given by:(9)σx=σy=2c1(λ2−1λ4)+2c2(λ4−1λ2)

In this step, the material parameters c1 and c2 must ensure that the stress response exhibits a monotonically increasing behavior under both uniaxial and biaxial tensile loading conditions, being c1 > 0. In both cases, a penalty factor of 10²⁰ has been included to enforce the solution.

In step 2, k1 and k2 parameters are calculated considering uniaxial and/or equi-biaxial experiment data with c1 and c2 values obtained from step 1 and adding the anisotropic term in a strain range above the defined threshold (λ = 1.02). The mathematical formulation of the Cauchy stress tensor differs depending on the loading condition. In the uniaxial case, assuming deformation in the x-direction and zero lateral stresses (σy=σz=0), the Cauchy stress is given by:(10)σx=2c1(λ2−1λ)+2c2(λ−1λ2)+2k1λ2(λ2−1)ek2(λ2−1)2whereas in the equi-biaxial case, assuming deformation in the x-direction and y-direction and zero stress in the z-direction, the Cauchy stress is given by:(11)σx=σy=2c1(λ2−1λ4)+2c2(λ4−1λ2)+4k1λ2(λ2−1)ek2(λ2−1)2.

The anisotropic term incorporates the effects due to collagen fiber recruitment, which becomes significant at higher stretch levels, incorporating the characteristic nonlinear stiffening observed in experimental data. In this step, a mathematical condition of k1 > 0 and k2 > 0 is imposed with a penalty factor of 10²⁰ to enforce a monotonically increasing behavior under both uniaxial and biaxial tensile loading conditions.

In step 3, an optimization process has been applied to improve the results considering the complete curve. This procedure has been applied considering previously calculated values of c1, c2, k1 and k2 as initial values of the iterative process. In case of c1, c2, k1, the refined optimization is substantiated due to the mathematical influence of the parameters at low stretch ratio of the uniaxial and equi-biaxial curve that can be calculated considering the derivative of the uniaxial and equi-biaxial Cauchy stress expression given respectively in Eqs. (12) and (13) with respect to the stretch ratio λ. The derivative value at λ = 1, which considers the initial slope of the stress–stretch curve, depends not only on the isotropic parameters c1 and c2 but also on the anisotropic parameter k1 as can be observed in the following equation:(12)dσdλ|λ=1=6c1+6c2+4k1 (13)dσdλ|λ=1=12c1+12c2+8k1

The exponential term associated with k2 vanishes when λ = 1 because its derivative is proportional to (λ2 − 1), which is zero at that point. Consequently, k2 has no influence at low stretch ratio, confirming that it only affects the stress behavior at higher deformations. This highlights a critical insight: From a mathematical point of view, k1 cannot be neglected in the early stretch domain, even though it is typically associated with fiber recruitment at larger deformations. This result emphasizes the mathematical importance of incorporating the anisotropic parameter k1 in the early stages of stress-stretch behavior, especially when interpreting or calibrating experimental data within the physiological range, and the need to incorporate a refined optimization process when decoupled material properties are calculated. Although in the mathematical model k1 has a constant value, this value considers the stiffness influence of fibers depending on the fibers’ recruitment and preferential orientation. In this research work, the hypothesis that in the proximity of λ = 1 the contribution to the derivative of the anisotropic component cannot be greater than the contribution of the isotropic component has been considered with the inclusion of a penalty factor of 10²⁰ in case that this value exceeds the value of the isotropic component according to Eqs. (14) and (15), considering uniaxial and biaxial tensile tests, respectively.

Penalty factor in case that (14)4k1≥12c1+12c2

(Uniaxial test)

Penalty factor in case that (15)8k1≥12c1+12c2

(Biaxial test)

The simultaneous optimization of all parameters, even when initialized from reasonable starting values, may lead to significant variations due to their intrinsic mathematical interdependence. Therefore, in this study, constraints are imposed such that the optimized values of c1 and c2 must yield at least 95% of the coefficient of determination obtained in Step 1, with a penalty factor of 10²⁰ in case this value is lower according to Eq. (16), while also penalizing large individual variations of these parameters through a penalty factor applied to the sum of the squares of their variations according to Eq. (17):

Penalty factor in case that (16)RSTEP32≥0.95⋅RSTEP12 (17)Penalty factor=1010((C1STEP3−C1STEP2C1STEP2)2(C2STEP3−C2STEP2C2STEP2)2)

This strategy ensures consistency in the isotropic parameters identified in Step 1 for λ ≤ 1.02, improving at the same time the fitting of the global test curve where the k2 value has greater importance at high stretch level, adapting the curvature through the exponential term.

In Step 4, the final proposed material parameters are evaluated by computing the determination coefficient (R²), the residual sum of squares (SS_Res), and the total variance in experimental data (SS_Tot).

The optimization of the constitutive parameters was performed using a global evolutionary algorithm based on the differential evolution (DE) method, as implemented in the SciPy optimization library. The DE algorithm employed a population-based stochastic search with the default “best1bin” mutation strategy and binomial crossover scheme. A maximum number of 10,000 iterations was specified, together with a convergence tolerance on the objective function of 10−9. A fixed random seed (seed = 1) was used to ensure reproducibility, and a final local refinement step (“polish”) was applied to enhance solution accuracy. The cost function was defined as a weighted least-squares error between experimental and model-predicted stresses, incorporating additional physically motivated constraints such as parameter positivity, monotonicity of the stress–strain response, and controlled variation between optimization steps. This procedure ensures a stable and physically consistent identification of the model parameters across different deformation regimes.

The generation of patient-specific computational models enabling the biomechanical simulation of corneal tissue for clinical and/or surgical purposes can be addressed through finite element formulations that achieve an adequate balance between accuracy and computational cost [45]. This requires constitutive models that account for the anisotropic hyperelastic behavior of the corneal stroma, typically characterized by a large number of parameters whose identification, when relying on a single type of mechanical test, constitutes an ill-posed problem [20], as multiple parameter sets may yield similar responses [19]. Additionally, the wide variety of available constitutive models [47] complicates cross-study comparisons [10,48], making it necessary to establish a common framework that allows the stiffness of the selected material model to be compared while distinguishing the isotropic and anisotropic contributions. This limitation is directly evidenced in the present work, where different parameter sets were found to produce nearly identical stress–strain responses, highlighting the intrinsic non-uniqueness of the inverse identification problem even when using a structured multi-step optimization strategy.

Although anisotropic hyperelastic models based on fiber dispersion generally provide superior accuracy [41], their formulation relies on integrating complex functions derived from generalized distributions or from more rigorous patient-specific measurements [2,16], both of which increase the number of variables and the overall complexity of parameter estimation, hindering comparability. This limitation becomes particularly relevant in the present study, where the use of a simplified invariant-based formulation allows a controlled analysis of parameter contributions while still capturing the main nonlinear response.

Xu et al. [49] showed that the dispersion parameter influence can be neglected in the total apical displacement, whereas it is important for corneal aberrations at physiological states. This observation is consistent with the present results, where the anisotropic contribution was found to influence the mechanical response from the very early stages of deformation, despite being traditionally associated with higher stretch levels.

In this context and considering the limited strain range experienced by the cornea under physiological conditions [21], behaving as a membrane subjected to a biaxial loading state, together with its quasi-incompressible nature and the preferential collagen fiber orientation in the central region along the nasal–temporal and superior–inferior directions [50], supports the use of invariant-based formulations that enable inter-study comparison using simpler and more standardized mechanical tests [24] like uniaxial and equi-biaxial tests. This approach was used in a previous investigation by our research group [19] considering the Holzapfel–Gasser–Ogden (HGO) model [15]; however, previous studies did not explicitly analyze the individual contribution of each parameter, nor the potential coupling between isotropic and anisotropic components under physiological strain levels, which is addressed in the present work [21].

The analysis of parameter contributions represents a critical aspect in understanding the mechanical response of the cornea. While isotropic and anisotropic components both contribute to the overall stress field, the present results show that the parameter k₁ influences the response from the early stages of deformation, whereas k₂ becomes relevant at higher stretch levels, governing the exponential stiffening behavior [49]. However, the residual analysis reveals systematic deviations across the stretch range, indicating that the current HGO formulation does not fully capture the transition between matrix-dominated and fiber-dominated regimes. This suggests that, despite accurate global fitting metrics, the model exhibits limitations in representing the detailed curvature of the experimental response, particularly in intermediate deformation ranges

Pandolfi and Manganiello [10] show different parameter sets based on the application of a Holzapfel Gasser Ogden model to the results of different tests. However, there is a lack of information on the strain rates used in these tests that affect the results obtained [51]. In the present work, the use of consistent experimental datasets [24] and a controlled strain rate [21] allows a more reliable comparison of material parameters, reducing one of the main sources of variability reported in the literature.

Considering the results obtained in this research work, the isotropic contribution, characterized by the shear modulus c1 + c2 = 0.08 MPa for the reference configuration and c1+c2=0.0797 MPa for the translated solution, falls within the range of values reported in recent studies [41] and remains consistent with experimentally measured shear properties of corneal tissue [52]. However, it should be noted that the parameter c2 takes negative values in the original configuration (c2=−0.0268 MPa), which may raise concerns regarding material stability. In the present work, this issue has been explicitly addressed by enforcing monotonicity conditions on the isotropic contribution, ensuring that the resulting stress–strain response remains physically admissible and strictly increasing over the considered stretch range. The introduction of negative c2 values is therefore justified as a modelling strategy to improve the fitting accuracy in the low-strain regime, where the experimental response is particularly sensitive to the initial curvature of the stress–strain curve.

Nonlinear parameters show differences when compared with other investigations [10,41] that can be attributed to the strain rate implemented in biomechanical tests and the optimization process. Particularly, the nonlinear parameter k2 = 36.3 is notably lower than values reached in other investigations considering inflation tests (200 [41]–750 [13]) but remains consistent with values derived from uniaxial or biaxial tests [10]; this result reflects the moderate level of exponential stiffening observed within the physiological strain range, where fiber recruitment is still limited. Additionally, the parameter identification process reveals a strong sensitivity to the reference configuration, as demonstrated by the translated solution, which produces a similar fitting quality (R2=0.991) with a different parameter set. This behaviour confirms the inherent non-uniqueness of the inverse problem, as multiple combinations of c₁, c₂, k₁ and k₂ are able to reproduce the experimental response with comparable accuracy. Furthermore, the reliance on R2 alone may lead to misleading conclusions regarding model performance, as evidenced by the residual analysis, which reveals systematic deviations across the stretch range despite the high goodness-of-fit values. These findings confirm that additional metrics and qualitative assessments are required to properly evaluate nonlinear constitutive models. The physiological relevance of the identified parameters is supported by previous work from Jan and Sigal [31], who demonstrated that collagen fiber recruitment becomes significant only above intraocular pressure thresholds of approximately 10–15 mmHg, corresponding to stretch levels of λ=1.02, in agreement with thin-shell mechanical models of the cornea subjected to biaxial loading conditions [32].

As an application case, the identified material parameters were implemented in a patient-specific corneal finite element model of a healthy subject. The simulation results produced apical displacement values within the expected physiological range under an intraocular pressure of 16 mmHg, reaching a maximum value of 0.244 mm, in agreement with previous experimental observations in healthy human corneas [13]. The mechanical response is governed by an isotropic contribution characterized by an effective shear modulus c1+c2=0.080 MPa, which is consistent with reported values for corneal tissue stiffness, ensuring a realistic representation of the initial deformation regime. In terms of stress distribution, the model predicts peak values of approximately 20 kPa in the central region under physiological loading conditions, with a spatial pattern exhibiting clear anatomical alignment with collagen fiber orientations, where preferential load transmission occurs along the nasal–temporal and superior–inferior directions, in agreement with previous investigations. Additionally, the nonlinear response governed by the anisotropic parameters (k1 and k2) leads to a progressive stiffening of the tissue at higher strain levels, although the relatively low values of k2 reflect a moderate exponential behavior consistent with the physiological deformation range considered. Despite these consistent global results, it should be noted that the parameter identification is not unique, as different parameter sets provide similar displacement and stress fields, highlighting the need for complementary validation metrics and reinforcing the importance of cautious interpretation of model predictions.

Overall, this research proposes a structured and physiologically grounded methodology for corneal parameter identification, combining experimental fitting and computational application within a finite element framework. However, the results also reveal inherent limitations related to parameter non-uniqueness and constitutive model sensitivity, which are not always fully addressed in existing literature. These findings emphasize the necessity of incorporating additional validation tools, such as residual analysis and sensitivity studies, to improve the robustness of biomechanical models. As a natural extension of the present work, future research will focus on the experimental validation of the proposed framework through direct comparison with patient-specific inflation tests and in vivo measurements, enabling a more rigorous assessment of the predictive capability of the identified parameters. In addition, further developments will consider the incorporation of advanced constitutive formulations accounting for progressive collagen recruitment and viscoelastic effects, as well as uncertainty quantification strategies to evaluate the robustness of the parameter identification process. These improvements are expected to strengthen the physiological relevance of the model and enhance its applicability in clinically oriented, patient-specific corneal biomechanics simulations.

This study presents a structured and computationally efficient framework for corneal material parameter identification based on the HGO hyperelastic formulation, enabling the consistent integration of uniaxial and biaxial experimental data through a multi-step calibration strategy. The introduction of a strain threshold allows the separation of matrix- and fiber-dominated mechanical regimes, providing a stable and accurate representation of the corneal response within the physiological deformation range, including the early contribution of anisotropic components and the progressive nonlinear stiffening associated with collagen fiber recruitment. The identified material parameters yield shear moduli and stress–strain responses consistent with reported corneal biomechanics, and their implementation in a patient-specific finite element model produces physiologically realistic displacement and stress distributions aligned with collagen fiber architecture. These results demonstrate the capability of the proposed methodology to support reliable in silico simulations and enhance inter-study comparability. Moreover, the framework offers a solid basis for future advancements, including the incorporation of microstructure-informed formulations, refined constitutive models accounting for fiber recruitment and dispersion, and uncertainty quantification strategies, thereby strengthening its applicability for both physiological analysis and patient-specific clinical applications in corneal biomechanics.