بدائل البحث:
coding algorithm » cosine algorithm (توسيع البحث), modeling algorithm (توسيع البحث), finding algorithm (توسيع البحث)
study algorithm » wsindy algorithm (توسيع البحث), td3 algorithm (توسيع البحث), seu algorithm (توسيع البحث)
element study » relevant study (توسيع البحث), present study (توسيع البحث), recent study (توسيع البحث)
complement 5a » complement _ (توسيع البحث), complement low (توسيع البحث)
5a algorithm » coa algorithm (توسيع البحث), sac algorithm (توسيع البحث), _ algorithm (توسيع البحث)
coding algorithm » cosine algorithm (توسيع البحث), modeling algorithm (توسيع البحث), finding algorithm (توسيع البحث)
study algorithm » wsindy algorithm (توسيع البحث), td3 algorithm (توسيع البحث), seu algorithm (توسيع البحث)
element study » relevant study (توسيع البحث), present study (توسيع البحث), recent study (توسيع البحث)
complement 5a » complement _ (توسيع البحث), complement low (توسيع البحث)
5a algorithm » coa algorithm (توسيع البحث), sac algorithm (توسيع البحث), _ algorithm (توسيع البحث)
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Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption
منشور في 2025"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"
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191
Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption
منشور في 2025"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"
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192
Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption
منشور في 2025"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"
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193
Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption
منشور في 2025"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"
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194
Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption
منشور في 2025"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"
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195
Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption
منشور في 2025"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"
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196
Multimaterial Metamaterial Inverse Design via Machine Learning for Tailorable and Reusable Energy Absorption
منشور في 2025"…Our method integrates multimaterial compatibility (TPU/resin/NiTi/Al alloy) with topology-morphing body-centered cubic (BCC) lattices, where nodal coordinates, beam diameters, and material parameters are co-optimized. We delve into studying the effects of material parameters, nodal coordinates, and beam diameter variations on the structural compressive performances by conducting over 20,000 simulation experiments on randomly generated BCC lattice structures using a finite element analysis. …"
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197
Table 1_WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.xlsx
منشور في 2025"…We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). …"
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198
Image 1_WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.tif
منشور في 2025"…We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). …"
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199
Table 2_WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq.docx
منشور في 2025"…We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). …"
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200
Addressed system.
منشور في 2024"…The proposed setup includes three key elements: a PV system emulator employing back converter control to replicate PV panel characteristics, a boost converter with an MPPT algorithm for efficient power tracking across diverse conditions, and a motor pump (MP) emulator integrating an induction motor connected to a DC generator to simulate water pump behaviors. …"